,,,,,,Technical Replicability,,,,,,,Statistical Replicability,
Name,Domain,Source,Year,Link,Purely Theoretical?,Code Available (1/0),Dataset Names,Dataset Containing PHI or de-identified PHI,Public,PHI & Public,Link/Ref,Comment,Variance/CI Reported,
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Agudo_Image_Collection_Pop-Up_CVPR_2018_paper.html,0,0,PASCAL VOC,0,1,0,,,0,1
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Agudo_Image_Collection_Pop-Up_CVPR_2018_paper.html,0,0,MUCT,0,,,,,,0
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Agudo_Image_Collection_Pop-Up_CVPR_2018_paper.html,0,0,TigDog,0,,,,,,0
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Agudo_Image_Collection_Pop-Up_CVPR_2018_paper.html,0,0,ASL Collection,0,,,,,,0
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Agudo_Image_Collection_Pop-Up_CVPR_2018_paper.html,0,0,synthetic,0,,,,,,0
Guide Me: Interacting With Deep Networks,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Rupprecht_Guide_Me_Interacting_CVPR_2018_paper.html,0,0,COCO-Stuff,0,1,0,,,0,1
Guide Me: Interacting With Deep Networks,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Rupprecht_Guide_Me_Interacting_CVPR_2018_paper.html,0,0,PASCAL VOC,0,1,0,,,,0
Learning Sentiment Memories for Sentiment Modification without Parallel Data,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1138/d18-1138,0,1,Yelp Review Dataset,0,1,0,,,0,1
Simple Recurrent Units for Highly Parallelizable Recurrence,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1477/d18-1477,0,1,"movie review sentiment (MR; Pang and Lee, 2005)",0,1,0,,,1,1
Simple Recurrent Units for Highly Parallelizable Recurrence,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1477/d18-1477,0,1,"sentence subjectivity (SUBJ; Pang and Lee, 2004)",0,1,0,,,,0
Simple Recurrent Units for Highly Parallelizable Recurrence,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1477/d18-1477,0,1,"customer reviews polarity (CR; Hu and Liu, 2004)",0,1,0,,,,0
Simple Recurrent Units for Highly Parallelizable Recurrence,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1477/d18-1477,0,1,"question type (TREC; Li and Roth, 2002)",0,1,0,,,,0
Simple Recurrent Units for Highly Parallelizable Recurrence,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1477/d18-1477,0,1,"opinion polarity (MPQA; Wiebe et al., 2005)",0,1,0,,,,0
Simple Recurrent Units for Highly Parallelizable Recurrence,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1477/d18-1477,0,1,"Stanford sentiment treebank (SST; Socher et al., 2013)",0,1,0,,,,0
Dating Documents using Graph Convolution Networks,NLP,ACL,2018,https://acl2018.org/paper/1683,0,1,Gigaword Corpus Associated Press Worldstream (APW),0,1,0,,,0,1
Dating Documents using Graph Convolution Networks,NLP,ACL,2018,https://acl2018.org/paper/1683,0,1,Gigaword Corpus New York Times (NYT),0,1,0,,,,0
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures,NLP,ACL,2018,http://aclweb.org/anthology/P18-1167,0,1,IWSLT EN→DE,0,1,0,,,1,1
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures,NLP,ACL,2018,http://aclweb.org/anthology/P18-1167,0,1,WMT’17 EN→DE,0,1,0,,,,0
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures,NLP,ACL,2018,http://aclweb.org/anthology/P18-1167,0,1,WMT’17 LV→EN,0,1,0,,,,0
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition,"NLP, ML4H",NAACL,2018,https://aclanthology.info/papers/N18-1001/n18-1001,0,0,WeiboNER,0,1,0,,,1,1
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition,"NLP, ML4H",NAACL,2018,https://aclanthology.info/papers/N18-1001/n18-1002,0,0,SighanNER,0,1,0,,,,0
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition,"NLP, ML4H",NAACL,2018,https://aclanthology.info/papers/N18-1001/n18-1003,0,0,TwitterNER,0,1,0,,,,0
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition,"NLP, ML4H",NAACL,2018,https://aclanthology.info/papers/N18-1001/n18-1004,0,0,CoNLL 2003 English NER,0,1,0,,,,0
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition,"NLP, ML4H",NAACL,2018,https://aclanthology.info/papers/N18-1001/n18-1005,0,0,Chinese medical NER,1,0,0,,Self-collected,,0
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1002/n18-1002,0,1,FIGER(GOLD),0,1,0,,,1,1
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1002/n18-1003,0,1,OntoNotes,0,1,0,,,,0
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo,General,NeurIPS,2018,https://papers.nips.cc/paper/8010-beyond-log-concavity-provable-guarantees-for-sampling-multi-modal-distributions-using-simulated-tempering-langevin-monte-carlo.pdf,1,0,,,,,,,,1
Neural Ordinary Differential Equations,General,NeurIPS,2018,https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf,0,1,Simulation Data Only,,,,,,0,1
Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima,General,ICML,2018,http://proceedings.mlr.press/v80/du18b.html,1,0,,,,,,,,1
Thompson Sampling for Combinatorial Semi-Bandits,General,ICML,2018,http://proceedings.mlr.press/v80/wang18a.html,1,0,,,,,,,,1
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Tem,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/hetero-convlstm-a-deep-learning-approach-to-traffic-accident-prediction-on-,0,0,Motor Vehicle Crash Data Iowa DOT,0,1,0,,,0,1
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Tem,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/hetero-convlstm-a-deep-learning-approach-to-traffic-accident-prediction-on-,0,0,High resolution rainfall data,0,1,0,,,,0
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Tem,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/hetero-convlstm-a-deep-learning-approach-to-traffic-accident-prediction-on-,0,0,RWIS Data (Iowa DOT),0,1,0,,,,0
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Tem,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/hetero-convlstm-a-deep-learning-approach-to-traffic-accident-prediction-on-,0,0,Road Networks,0,1,0,,,,0
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Tem,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/hetero-convlstm-a-deep-learning-approach-to-traffic-accident-prediction-on-,0,0,Satellite Images,0,1,0,,,,0
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Tem,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/hetero-convlstm-a-deep-learning-approach-to-traffic-accident-prediction-on-,0,0,Traffic Camera Data,0,1,0,,,,0
Deep Recursive Network Embedding with Regular Equivalence,General,KDD,2018,http://delivery.acm.org/10.1145/3230000/3220068/p2357-tu.pdf?ip=76.71.73.74&id=3220068&acc=OPENTOC&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E054E54E275136550&__acm__=1551578034_a65909f657cae10bf71734fe88471b06,0,0,Karate Network,0,1,0,,,0,1
Deep Recursive Network Embedding with Regular Equivalence,General,KDD,2018,http://delivery.acm.org/10.1145/3230000/3220068/p2357-tu.pdf?ip=76.71.73.74&id=3220068&acc=OPENTOC&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E054E54E275136550&__acm__=1551578034_a65909f657cae10bf71734fe88471b07,0,0,Barbell Network,0,1,0,,,,0
Deep Recursive Network Embedding with Regular Equivalence,General,KDD,2018,http://delivery.acm.org/10.1145/3230000/3220068/p2357-tu.pdf?ip=76.71.73.74&id=3220068&acc=OPENTOC&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E054E54E275136550&__acm__=1551578034_a65909f657cae10bf71734fe88471b08,0,0,Jazz,0,1,0,,,,0
Deep Recursive Network Embedding with Regular Equivalence,General,KDD,2018,http://delivery.acm.org/10.1145/3230000/3220068/p2357-tu.pdf?ip=76.71.73.74&id=3220068&acc=OPENTOC&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E054E54E275136550&__acm__=1551578034_a65909f657cae10bf71734fe88471b09,0,0,BlogCatalog,0,1,0,,,,0
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.html,0,1,Webvision,0,1,0,,,0,1
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.html,0,1,ImageNet,0,1,0,,,,0
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.html,0,1,Clothing-1M,0,1,0,,,,0
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.html,0,1,Food-101,0,1,0,,,,0
A Unified Framework for Multi-View Multi-Class Object Pose Estimation,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chi_Li_A_Unified_Framework_ECCV_2018_paper.html,0,0,YCB-Video,0,1,0,,,0,1
A Unified Framework for Multi-View Multi-Class Object Pose Estimation,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chi_Li_A_Unified_Framework_ECCV_2018_paper.html,0,0,JHUScene-50,0,1,0,,,,0
A Unified Framework for Multi-View Multi-Class Object Pose Estimation,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chi_Li_A_Unified_Framework_ECCV_2018_paper.html,0,0,ObjectNet-3D,0,1,0,,,,0
Learning Deep Neural Networks for Vehicle Re-ID With Visual-Spatio-Temporal Path Proposals,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Shen_Learning_Deep_Neural_ICCV_2017_paper.html,0,0,VeRi-776,0,1,0,,,0,1
Weakly Supervised Summarization of Web Videos,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Panda_Weakly_Supervised_Summarization_ICCV_2017_paper.html,0,0,CoSum,0,1,0,,,0,1
Weakly Supervised Summarization of Web Videos,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Panda_Weakly_Supervised_Summarization_ICCV_2017_paper.html,0,0,TVSum,0,1,0,,,0,0
Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU,ML4H,mlhc2018,2018,,0,0,Pediatric ICU at the Hospital for Sick Children in Toronto,1,0,0,,,0,1
Boosted Trees for Risk Prognosis,ML4H,mlhc2018,2018,,0,0,Meta-analysis Global Group in Chronic heart failure database,1,0,0,,,1,1
Boosted Trees for Risk Prognosis,ML4H,mlhc2018,2018,,0,0,UK Biobank,1,1,1,,,1,0
Boosted Trees for Risk Prognosis,ML4H,mlhc2018,2018,,0,0,United Network for Organ Sharing (UNOS) database,1,0,0,,,1,0
Racial Disparities and Mistrust in End-of-Life Care,ML4H,mlhc2018,2018,,0,1,MIMIC-III v1.4,1,1,1,,,1,1
Sequential Pattern Analysis on Neurosurgical Simulation Data,ML4H,mlhc2018,2018,,0,0,NeuroTouch simulation performance data,0,0,0,,,1,1
Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy,ML4H,mlhc2018,2018,,0,0,"acquired at Ospedale Ca’ Granda Niguarda, Milan (Italy)",1,0,0,,,1,1
Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis,ML4H,mlhc2018,2018,http://proceedings.mlr.press/v85/durand18a/durand18a.pdf,0,0,experiment,0,0,0,,,1,1
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model,ML4H,mlhc2018,2018,,0,0,experiment,1,0,0,,,0,1
"Modeling ""Presentness"" of Electronic Health Record Data to Improve Patient State Estimation",ML4H,mlhc2018,2018,,0,0,a general pediatric intensive care unit (PICU),1,,,,,0,1
Learning to Summarize Electronic Health Records Using Cross-Modality Correspondences,ML4H,mlhc2018,2018,,0,0,MIMIC-III v1.4,1,1,1,,,1,1
Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings,ML4H,mlhc2018,2018,,0,1,2017 Physionet challenge,0,1,0,,,0,1
3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities,ML4H,mlhc2018,2018,,0,0,collected in a clinician-guided simulation setting,0,0,0,,,0,1
Reproducible Survival Prediction with SEER Cancer Data,ML4H,mlhc2018,2018,,0,1,SEER Cancer data,1,1,1,,* a survey paper,0,1
Bayesian Trees for Automated Cytometry Data Analysis,ML4H,mlhc2018,2018,,0,0,acute myeloid leukemia (AML),0,1,0,,,0,1
Bayesian Trees for Automated Cytometry Data Analysis,ML4H,mlhc2018,2018,,0,0,bone marrow mononuclear cells (BMMC),0,1,0,,,0,0
Disease-Atlas: Navigating Disease Trajectories using Deep Learning,ML4H,mlhc2018,2018,,0,0,UK Cystic Fibrosis Trust,1,1,1,,,1,1
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks,ML4H,mlhc2018,2018,,0,0,217 oropharyngeal cancer patients,1,0,0,,,0,1
Representational Learning Approaches for ECG Dynamics to Detect False Arrhythmia Alarms,ML4H,mlhc2018,2018,,0,0,MIT-BIH Arrhythmia Dataset,0,1,0,,,1,1
Representational Learning Approaches for ECG Dynamics to Detect False Arrhythmia Alarms,ML4H,mlhc2018,2018,,0,0,PhysioNet Challenge 2015 Dataset,0,1,0,,,1,0
"Deep Spine: Automated Lumbar Vertebral Segmentation, Disc-Level Designation, and Spinal Stenosis Grading Using Deep Learning",ML4H,mlhc2018,2018,,0,0,acquired at Massachusetts General Hospital (MGH) Department of Radiology,1,0,0,,,1,1
Integrating Hypertension Phenotype and Genotype with Hybrid Non-negative Matrix Factorization,ML4H,mlhc2018,2018,https://arxiv.org/abs/1805.05008,0,0,Hypertension Genetic Epidemiology Network (HyperGEN) study,1,1,1,,,0,1
Integrating Hypertension Phenotype and Genotype with Hybrid Non-negative Matrix Factorization,ML4H,mlhc2018,2018,https://arxiv.org/abs/1805.05008,0,0,Simulated Data,0,0,0,,,0,0
Computer Vision-based Descriptive Analytics of Seniors' Daily Activities for Long-term Health Monitoring,ML4H,mlhc2018,2018,,0,0,conduct this study in an assisted living facility for seniors,1,0,0,,,0,1
Integrating Machine Learning and Optimization Methods for Imaging of Patients with Prostate Cancer,ML4H,mlhc2018,2018,,0,0,from a large state-wide prostate cancer collaborative,1,0,0,,,1,1
ConvSCCS: Convolutional Self-Controlled Case Series Model for Lagged Adverse Event Detection,ML4H,mlhc2018,2018,https://arxiv.org/pdf/1712.08243.pdf,0,0,French national health insurance database (SNIIRAM),1,1,1,,,1,1
ConvSCCS: Convolutional Self-Controlled Case Series Model for Lagged Adverse Event Detection,ML4H,mlhc2018,2018,https://arxiv.org/pdf/1712.08243.pdf,0,0,simulation dataset,0,0,0,,,1,0
Deep Survival Analysis: Nonparametrics and Missingness,ML4H,mlhc2018,2018,,0,0,a large metropolitan hospital,1,0,0,,,0,1
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks,ML4H,mlhc2018,2018,,0,0,MIMIC-III v1.4,1,1,1,,,1,1
Learning from the experts: From diagnostic expert systems to machine learning diagnosis models,ML4H,mlhc2018,2018,,0,0,MIMIC-III,1,1,1,,,0,1
Learning from the experts: From diagnostic expert systems to machine learning diagnosis models,ML4H,mlhc2018,2018,,0,0,from case simulator,0,0,0,,,0,0
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition,ML4H,mlhc2018,2018,,0,0,NCBI-diseas,0,1,0,,,0,1
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition,ML4H,mlhc2018,2018,,0,0,BC2GM,0,1,0,,,0,0
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition,ML4H,mlhc2018,2018,,0,0,JNLPBA,0,1,0,,,0,0
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition,ML4H,mlhc2018,2018,,0,0,BioCreative V Chemical Disease Relation Extraction (BC5CDR) task,0,1,0,,,0,0
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images,ML4H,mlhc2018,2018,,0,1,Philadelphia Neurodevelopmental Cohort (PNC) study,1,1,1,,,1,1
Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data,ML4H,mlhc2018,2018,https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/5b73739b40ec9a45a95436a1/1534292893333/27.pdf,0,0,recruited participants,1,0,0,,,1,1
Preference Learning in Assistive Robotics: Observational Repeated Inverse Reinforcement Learning,ML4H,mlhc2018,2018,,0,0,recruited participants,1,0,0,,,0,1
Reinforcement Learning with Action-Derived Rewards for Chemotherapy and Clinical Trial Dosing Regimen Selection,ML4H,mlhc2018,2018,,0,0,50 simulated patients,0,0,0,,,1,1
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models,ML4H,mlhc2018,2018,,0,0,a real EHR cohort collected from patients at Duke Hospital,1,0,0,,,1,1
Chronic Disease Prediction Using Medical Notes,ML4H,mlhc2018,2018,,0,1,NYU Langone Hospital EHR system,1,0,0,,*contains code for generating synthetic data,0,1
Piecewise-constant parametric approximations for survival learning:,ML4H,mlhc2017,2017,,0,0,MIMIC-III v1.4,1,1,1,,,1,1
Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing,ML4H,mlhc2017,2017,,0,0,recruited participants,1,0,0,,,0,1
Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data:,ML4H,mlhc2017,2017,,0,0,SEER registry data with the Medical Provider Analysis and Review,1,1,1,,,0,1
Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning model,ML4H,mlhc2017,2017,,0,0,Cardiothoracic Anesthesiology Registry (CAROLA),1,0,0,,,1,1
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information:,ML4H,mlhc2017,2017,,0,0,1926 patients collected as part of the Osteoarthritis Initiative (OAI),1,0,0,,,0,1
Towards Vision-based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance,ML4H,mlhc2017,2017,,0,0,collected from two hospitals,1,0,0,,,0,1
Surgeon Technical Skill Assessment using Computer Vision based Analysis,ML4H,mlhc2017,2017,,0,0,recruited participants,1,0,0,,,0,1
Predicting Surgery Duration with Neural Heteroscedastic Regression:,ML4H,mlhc2017,2017,,0,0,"patient records extracted from the EHR system of the University of California, San Diego Health System",1,0,0,,,0,1
Temporal prediction of multiple sclerosis evolution from patient-centered outcomes,ML4H,mlhc2017,2017,,0,0,PCO data set acquired from a cohort of PwMS progressively enrolled within an ongoing funded project,1,0,0,,,1,1
Clustering Patients with Tensor Decomposition,ML4H,mlhc2017,2017,,0,0,a real-world EHR,1,0,0,,,0,1
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach:,ML4H,mlhc2017,2017,,0,0,MIMIC-III v1.4,1,1,1,,,1,1
Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Model,ML4H,mlhc2017,2017,,0,0,PRAEGNANT study network,1,1,1,,,1,1
Patient Similarity Using Population Statistics and Multiple Kernel Learning,ML4H,mlhc2017,2017,,0,0,e-icu,1,1,1,,,1,1
A Video-Based Method for Automatically Rating Ataxia,ML4H,mlhc2017,2017,,0,0,The BARS Dataset,1,0,0,,,1,1
Visualizing Clinical Significance with Prediction and Tolerance Regions:,ML4H,mlhc2017,2017,,0,0,Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE),1,0,0,,,0,1
Predictive Hierarchical Clustering: Learning clusters of CPT codes for improving surgical outcomes,ML4H,mlhc2017,2017,,0,0,simulate data,0,0,0,,,0,1
Predictive Hierarchical Clustering: Learning clusters of CPT codes for improving surgical outcomes,ML4H,mlhc2017,2017,,0,0,American College of Surgeons’ NSQIP,1,0,0,https://www.facs.org/quality-programs/about/cqi/internetresources/databases,can be purchased,0,0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection,ML4H,mlhc2017,2017,,0,1,"university health system spanning 18 months, extracted directly from our Epic EHR",1,0,0,,,0,1
Marked Point Process for Severity of Illness Assessment,ML4H,mlhc2017,2017,,0,0,MIMIC-III v1.4,1,1,1,,,0,1
Marked Point Process for Severity of Illness Assessment,ML4H,mlhc2017,2017,,0,0,acquired at a tertiary pediatric intensive care unit,1,0,0,,,0,0
Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study,ML4H,mlhc2017,2017,,0,0,2015 TREC CDS track dataset,0,1,0,,,0,1
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks:,ML4H,mlhc2017,2017,,0,1,self-collected EHR,1,0,0,,,0,1
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks:,ML4H,mlhc2017,2017,,0,1,self-collected EHR,1,0,0,,,0,0
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks:,ML4H,mlhc2017,2017,,0,1,MIMIC-III v1.4,1,1,1,,,0,0
Quantifying Mental Health from Social Media using Learned User Embeddings:,ML4H,mlhc2017,2017,,0,1,CLPsych shared task data,0,1,0,,,0,1
Clinical Intervention Prediction and Understanding using Deep Networks,ML4H,mlhc2017,2017,,0,0,MIMIC-III v1.4,1,1,1,,,0,1
Understanding Coagulopathy using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach,ML4H,mlhc2017,2017,,0,0,colllected from hospital,1,0,0,,,0,1
Towards a directory of rare disease specialists: Identifying experts from publication history,ML4H,mlhc2017,2017,,0,1,positive disease-expert associations from GeneReviews publications were combined with the unlabeled disease-author associations from OMIM,0,0,0,,,0,1
Reproducibility in critical care: a mortality prediction case study:,ML4H,mlhc2017,2017,,0,1,MIMIC-III v1.4,1,1,1,,,0,1
Predicting Infant Motor Development Status using Day Long Movement Data from Wearable Sensors,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.02617.pdf,0,0,acquired from lab,1,0,0,,,0,1
Interpretable Patient Mortality Prediction with Multi-value Rule Sets,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.03633.pdf,0,1,real-world dataset from Nationwide Inpatient Sample,1,0,0,,,0,1
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning,ML4H,HD-KDD2018,2018,http://arxiv.org/pdf/1807.03159.pdf,0,0,Alzheimer’s Disease Neuroimaging Initiative (ADNI) study data,1,1,1,,,1,1
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1808.02602.pdf,0,0,"Synthetic Derivative (SD), a de-identified EHR database gathered at the VUMC",1,1,1,,,1,1
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1806.09542.pdf,0,0,MIMIC-III,1,1,1,,,1,1
Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer’s Disease,ML4H,HD-KDD2018,2018,http://arxiv.org/pdf/1807.01619.pdf,0,0,study conducted at the Faculty of Medicine of Lisbon,1,0,0,,,1,1
Multi-Task Learning with Incomplete Data for Healthcare,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.02442.pdf,0,0,Alzheimer’s disease data from ADNI,1,1,1,,,0,1
Mammography Dual View Mass Correspondence,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.00637.pdf,0,0,The Photo Tourism dataset,0,1,0,,,0,1
Mammography Dual View Mass Correspondence,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.00637.pdf,0,0,The Digital Database for Screening Mammography (DDSM),0,1,0,,,0,0
Mammography Dual View Mass Correspondence,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.00637.pdf,0,0,The In-house dataset,0,0,0,,,0,0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.01705.pdf,0,0,MIMIC-III,1,1,1,,,0,1
YouTube for Patient Education: A Deep Learning Approach for Understanding Medical Knowledge from User-Generated Videos,ML4H,HD-KDD2018,2018,http://arxiv.org/pdf/1807.03179.pdf,0,0,YouTube Videos,0,1,0,,,0,1
Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep Learning,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.04077.pdf,0,0,acquired patients data,1,0,0,,,0,1
Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.04667.pdf,0,0,acquired patients data,1,0,0,,,0,1
A hybrid deep learning approach for medical relation extraction,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1806.11189.pdf,0,0,a subset of i2b22010 challenge,0,1,0,,,0,1
Building a Controlled Vocabulary for Standardizing Precision Medicine Terms,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.01000.pdf,0,0,Precision Medicine Vocabulary (PMV),0,0,0,,,0,1
Measuring the quality of Synthetic data for use in competitions,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1806.11345.pdf,0,1,,0,0,0,,check this - on synthetic data,0,1
Generating Synthetic but Plausible Healthcare Record Datasets,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.01514.pdf,0,1,MIMIC-III,1,1,1,,,0,1
Generating Synthetic but Plausible Healthcare Record Datasets,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.01514.pdf,0,1,Hospital de la Santa Creu i Sant Pau in Barcelona,1,0,0,,,0,0
Mammography Assessment using Multi-Scale Deep Classifiers,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.03095.pdf,0,0,Digital Database for Screening of Mammography (DDSM),0,1,0,,,0,1
Synthetic Sampling for Multi-Class Malignancy Prediction,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.02608.pdf,0,0,NIH/NCI Lung Image Database Consortium (LIDC),0,1,0,,,0,1
PGLasso: Microbial Community Detection through Phylogenetic Graphical Lasso,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.08039.pdf,0,0,metagenomic data generated by the next-generation sequencing (NGS),0,1,0,https://www.hmpdacc.org/HMASM/,,1,1
Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1808.04411.pdf,0,0,Heart Sound & Murmur Library ,0,1,0,,,1,1
Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1808.04411.pdf,0,0,Classifying Heart Sounds Challenge,0,1,0,,,1,0
Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1808.04411.pdf,0,0,Physionet Challenge’ 2016 dataset,0,1,0,,,1,0
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology,ML4H,HD-KDD2018,2018,https://arxiv.org/pdf/1807.02599.pdf,0,0,"incident reports from Imperial College Healthcare NHS Trust, London",0,1,0,https://report.nrls.nhs.uk/nrlsreporting/,,0,1
Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.08592,0,0,http://dcapswoz.ict.usc.edu/,1,1,1,,"* Psychiatry, face scans and speech samples. Code not released but used public code for core repos.",0,1
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.10799,0,0,,,,,,,,1
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.09417,0,0,study conducteed at French university hospital,1,0,0,,,1,1
Clinical Concept Extraction with Contextual Word Embedding,ML4H,ml4h2018,2018,https://arxiv.org/abs/1810.10566,0,1,i2b2 2010 challenge dataset,0,1,0,,,1,1
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.10746,0,0,ADNI,1,1,1,,,0,1
Disease phenotyping using deep learning: A diabetes case study,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.11818,0,0,Cerner HealthFacts database,1,0,0,,Dataset is accessible to memebers of USC community,0,1
Glottal Closure Instants Detection From Pathological Acoustic Speech Signal Using Deep Learning,ML4H,ml4h2018,2018,https://arxiv.org/pdf/1811.09956.pdf,0,0,"Self-collected, B. C. Roy Technology Hospital,
Indian Institute of Technology Kharagpur, India",1,,,,,0,1
Model-Based Reinforcement Learning for Sepsis Treatment,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.09602,0,0,MIMIC-III,1,1,1,,,0,1
Patchnet: Interpretable Neural Networks for Image Classification,ML4H,ml4h2018,2018,https://arxiv.org/abs/1705.08078,0,0,ISBI-ISIC 2017 melanoma classification challenge,1,1,1,,,0,1
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.11958,0,0,"Self collected: Colorado State University College of Veterinary Medicine, discharge summaries from norcal, SAGE centers for veterinary medicine",1,0,0,,,0,1
Group induced graphical lasso allows for discovery of molecular pathways-pathways interactions,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.09673,0,0,https://portal.gdc.cancer.gov/projects/TARGET-NBL,1,1,1,,,0,1
A Framework for Implementing Machine Learning on Omics Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.10455,0,1,METABRIC microarray,0,1,0,,,0,1
A Framework for Implementing Machine Learning on Omics Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.10455,0,1,TCGA microarray,0,1,0,,,0,0
A Framework for Implementing Machine Learning on Omics Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.10455,0,1,TCGA RNA-seq,0,1,0,,,0,0
Population-aware Hierarchical Bayesian Domain Adaptation,ML4H,ml4h2018,2018,https://arxiv.org/pdf/1811.08579.pdf,0,0,GoViral,1,0,0,,I don't think this data is public but can't be sure.,0,1
Population-aware Hierarchical Bayesian Domain Adaptation,ML4H,ml4h2018,2018,https://arxiv.org/pdf/1811.08579.pdf,0,0,FluWatch,1,0,0,,I don't think this data is public but can't be sure.,0,0
Population-aware Hierarchical Bayesian Domain Adaptation,ML4H,ml4h2018,2018,https://arxiv.org/pdf/1811.08579.pdf,0,0,Hong Kong,1,0,0,,I don't think this data is public but can't be sure.,0,0
Population-aware Hierarchical Bayesian Domain Adaptation,ML4H,ml4h2018,2018,https://arxiv.org/pdf/1811.08579.pdf,0,0,Hutterite,1,0,0,,I don't think this data is public but can't be sure.,0,0
Modeling Irregularly Sampled Clinical Time Series,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00531,0,0,MIMIC-III,1,1,1,,,1,1
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00268,0,0,MIMIC-III,1,1,1,,,0,1
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.09950,0,0,"self-collected hand hygeine detection, ICU activity logging, and RGB Images (kincet trace logs + ICU activity logs from one institution)",1,0,0,,,0,1
Fast Learning-based Registration of Sparse Clinical Images,ML4H,ml4h2018,2018,https://arxiv.org/pdf/1812.06932.pdf,0,1,self-collected stroke dataset (3D MRI Scans),1,0,0,,,1,1
Improving Clinical Predictions through Unsupervised Time Series Representation Learning,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00490,0,0,eICU,1,1,1,,,1,1
Feature Selection Based on Unique Relevant Information for Health Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00415,0,0,Z-Alizadeh Sani,1,1,1,,,0,1
Feature Selection Based on Unique Relevant Information for Health Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00415,0,0,Breast Cancer,1,1,1,,,0,0
Feature Selection Based on Unique Relevant Information for Health Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00415,0,0,SPECTF Heart,1,1,1,,,0,0
Feature Selection Based on Unique Relevant Information for Health Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00415,0,0,EEG,0,1,0,,,0,0
Feature Selection Based on Unique Relevant Information for Health Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00415,0,0,Arrhythmia,1,1,1,,,0,0
Feature Selection Based on Unique Relevant Information for Health Data,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00415,0,0,Heart Disease,1,1,1,,,0,0
A Hybrid Instance-based Transfer Learning Method,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01063,0,0,LFPW,0,1,0,,,0,1
A Hybrid Instance-based Transfer Learning Method,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01063,0,0,Helen,0,1,0,,,0,0
A Hybrid Instance-based Transfer Learning Method,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01063,0,0,CK+,0,1,0,,,0,0
A Hybrid Instance-based Transfer Learning Method,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01063,0,0,iBUG,0,1,0,,,0,0
A Hybrid Instance-based Transfer Learning Method,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01063,0,0,AFW,0,1,0,,,0,0
A Hybrid Instance-based Transfer Learning Method,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01063,0,0,UNBC-McMaster Shoulder Pain Expression Archive,0,1,0,,,0,0
Multiple Instance Learning for ECG Risk Stratification,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00475,0,0,MERLIN-TIMI (http://www.timi.org/index.php?page=merlin-timi-36),1,0,0,,,0,1
Measuring the Stability of EHR- and EKG-based Predictive Models,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.00210,0,0,self-collected ED cohort data,1,0,0,,,1,1
"Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification",ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.11996,0,0,OASIS Brains Datasets,1,1,1,,,0,1
Learning Global Additive Explanations for Neural Nets Using Model Distillation,ML4H,ml4h2018,2018,https://arxiv.org/abs/1801.08640,0,0,UCI data sets - Bikeshare,0,1,0,,,1,1
Learning Global Additive Explanations for Neural Nets Using Model Distillation,ML4H,ml4h2018,2018,https://arxiv.org/abs/1801.08640,0,0,UCI data sets (Magic),0,1,0,,,1,0
Learning Global Additive Explanations for Neural Nets Using Model Distillation,ML4H,ml4h2018,2018,https://arxiv.org/abs/1801.08640,0,0,a Loan risk scoring data set from an online lending company,1,1,1,,,1,0
Learning Global Additive Explanations for Neural Nets Using Model Distillation,ML4H,ml4h2018,2018,https://arxiv.org/abs/1801.08640,0,0,the 2018 FICO Explainable ML Challenge’s credit data set,1,1,1,,,1,0
Learning Global Additive Explanations for Neural Nets Using Model Distillation,ML4H,ml4h2018,2018,https://arxiv.org/abs/1801.08640,0,0,pneumonia data set,0,1,0,,,1,0
Towards generative adversarial networks as a new paradigm for radiology education,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01547,0,0,Oakden Ryder Radiological Hip Fracture Dataset,1,0,0, https://arxiv.org/pdf/1711.06504.pdf,,0,1
Probabilistic Joint Face-Skull Modelling for Facial Reconstruction,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Madsen_Probabilistic_Joint_Face-Skull_CVPR_2018_paper.html,0,0,MRI Scans (self-collected),1,0,0,,,1,1
Probabilistic Joint Face-Skull Modelling for Facial Reconstruction,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Madsen_Probabilistic_Joint_Face-Skull_CVPR_2018_paper.html,0,0,3D Face Scans (self-collected),1,0,0,,,,0
Probabilistic Joint Face-Skull Modelling for Facial Reconstruction,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Madsen_Probabilistic_Joint_Face-Skull_CVPR_2018_paper.html,0,0,2D-constructed Faces (self-collected),1,0,0,,,,0
Human Semantic Parsing for Person Re-Identification,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kalayeh_Human_Semantic_Parsing_CVPR_2018_paper.html,0,0,Market-1501 ,0,1,0,,,0,1
Human Semantic Parsing for Person Re-Identification,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kalayeh_Human_Semantic_Parsing_CVPR_2018_paper.html,0,0,CUHK03,0,1,0,,,,0
Human Semantic Parsing for Person Re-Identification,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kalayeh_Human_Semantic_Parsing_CVPR_2018_paper.html,0,0,DukeMTMCreID,0,1,0,,,,0
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Noh_Improving_Occlusion_and_CVPR_2018_paper.html,0,0,Caltech pedestrian,0,1,0,,,0,1
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Noh_Improving_Occlusion_and_CVPR_2018_paper.html,0,0,CityPersons,0,1,0,,,,0
pOSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Hong_pOSE_Pseudo_Object_CVPR_2018_paper.html,0,0,dinosaur,0,1,0,,,0,1
pOSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Hong_pOSE_Pseudo_Object_CVPR_2018_paper.html,0,0,? Olsson’s [39],0,0,0,,,,0
pOSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Hong_pOSE_Pseudo_Object_CVPR_2018_paper.html,0,0,? Strecha et al.’s [47],0,0,0,,,,0
SSNet: Scale Selection Network for Online 3D Action Prediction,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_SSNet_Scale_Selection_CVPR_2018_paper.html,0,0,OAD,0,1,0,,,9,1
SSNet: Scale Selection Network for Online 3D Action Prediction,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_SSNet_Scale_Selection_CVPR_2018_paper.html,0,0,PKU-MMD,0,1,0,,,,0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html,0,0,ImageNet,0,1,0,,,0,1
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html,0,0,Caltech-256,0,1,0,,,,0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html,0,0,Oxford flowers 102,0,1,0,,,,0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html,0,0,Caltech-UCSD Birds-200-2011,0,1,0,,,,0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html,0,0,MIT Indoor 67 scene recognition dataset,0,1,0,,,,0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html,0,0,SUN attribute database,0,1,0,,,,0
RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Paschalidou_RayNet_Learning_Volumetric_CVPR_2018_paper.html,0,1,Aerial Dataset  Restrepo et al. [25],0,1,0,,,0,1
RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Paschalidou_RayNet_Learning_Volumetric_CVPR_2018_paper.html,0,1,DTU Dataset,0,1,0,,,,0
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Xie_Environment_Upgrade_Reinforcement_CVPR_2018_paper.html,0,0,MSCOCO,0,1,0,,,0,1
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Xie_Environment_Upgrade_Reinforcement_CVPR_2018_paper.html,0,0,MPII Human Pose,0,1,0,,,,0
Self-produced Guidance for Weakly-supervised Object Localization,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaolin_Zhang_Self-produced_Guidance_for_ECCV_2018_paper.html,0,1,ILSVRC,0,1,0,,,0,1
Deterministic Consensus Maximization with Biconvex Programming,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zhipeng_Cai_Deterministic_Consensus_Maximization_ECCV_2018_paper.html,0,1,NYC Library dataset,0,1,0,,More theoretically Oriented,0,1
Deterministic Consensus Maximization with Biconvex Programming,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zhipeng_Cai_Deterministic_Consensus_Maximization_ECCV_2018_paper.html,0,1,NotreDame dataset [24],0,1,0,,,,0
Deterministic Consensus Maximization with Biconvex Programming,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zhipeng_Cai_Deterministic_Consensus_Maximization_ECCV_2018_paper.html,0,1,Synthetic Data,,,,,,,0
HiDDeN: Hiding Data with Deep Networks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Jiren_Zhu_HiDDeN_Hiding_Data_ECCV_2018_paper.html,0,0,COCO,0,1,0,,,0,1
HiDDeN: Hiding Data with Deep Networks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Jiren_Zhu_HiDDeN_Hiding_Data_ECCV_2018_paper.html,0,0,BOSS dataset,0,1,0,,,,0
Synergy Between Face Alignment and Tracking via Discriminative Global Consensus Optimization,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Khan_Synergy_Between_Face_ICCV_2017_paper.html,0,0,300-VW,0,1,0,,,0,1
Synergy Between Face Alignment and Tracking via Discriminative Global Consensus Optimization,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Khan_Synergy_Between_Face_ICCV_2017_paper.html,0,0,Oxford-IIIT-Pet dataset,0,1,0,,,,0
Deep TextSpotter: An End-To-End Trainable Scene Text Localization and Recognition Framework,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Busta_Deep_TextSpotter_An_ICCV_2017_paper.html,0,1,ICDAR 2013,0,1,0,,,0,1
Deep TextSpotter: An End-To-End Trainable Scene Text Localization and Recognition Framework,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Busta_Deep_TextSpotter_An_ICCV_2017_paper.html,0,1,ICDAR 2015,0,1,0,,,,0
Anticipating Daily Intention Using On-Wrist Motion Triggered Sensing,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Wu_Anticipating_Daily_Intention_ICCV_2017_paper.html,0,1,Intention Dataset,0,1,0,,"New, self-collected, will release",0,1
3D Face Reconstruction from Light Field Images: A Model-free Approach,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Mingtao_Feng_3D_Face_Reconstruction_ECCV_2018_paper.html,0,0,BU-3DFE,0,1,0,,,1,1
3D Face Reconstruction from Light Field Images: A Model-free Approach,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Mingtao_Feng_3D_Face_Reconstruction_ECCV_2018_paper.html,0,0,BU-4DFE,0,1,0,,,,0
Face De-Spoofing: Anti-Spoofing via Noise Modeling,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper.html,0,0,CASIA-MFSD,0,1,0,,,0,1
Face De-Spoofing: Anti-Spoofing via Noise Modeling,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper.html,0,0,Replay-Attack,0,1,0,,,,0
Face De-Spoofing: Anti-Spoofing via Noise Modeling,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper.html,0,0,Oulu-NPU,0,1,0,,,,0
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zheng_Dang_Eigendecomposition-free_Training_of_ECCV_2018_paper.html,0,1,SUN3D,0,1,0,,,0,1
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zheng_Dang_Eigendecomposition-free_Training_of_ECCV_2018_paper.html,0,1,Simulated Plane Fitting Data,,,,,,,0
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zheng_Dang_Eigendecomposition-free_Training_of_ECCV_2018_paper.html,0,1,Simulated PnP Data,,,,,,,0
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.html,0,1,GTOT,0,1,0,,,0,1
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.html,0,1,RGBT210,0,1,0,,,,0
HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.html,0,0,DoubleFusion,0,1,0,,,1,1
HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.html,0,0,Hybrid Tracker ? [11],0,1,0,,,,0
"Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence",CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Park_Joint_Estimation_of_ICCV_2017_paper.html,0,0,Self-collected Real Data,0,0,0,,,0,1
"Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence",CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Park_Joint_Estimation_of_ICCV_2017_paper.html,0,0,Synthetic Datasets,0,0,0,,,,0
Turning Corners Into Cameras: Principles and Methods,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Bouman_Turning_Corners_Into_ICCV_2017_paper.html,0,0,Manually Collected Indoor,0,0,0,,More of a 'first in class' thing than a 'beat other models' thing,0,1
Turning Corners Into Cameras: Principles and Methods,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Bouman_Turning_Corners_Into_ICCV_2017_paper.html,0,0,Manually Collected Outdoor (varying weahter),0,0,0,,,,0
An Analysis of Visual Question Answering Algorithms,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Kafle_An_Analysis_of_ICCV_2017_paper.html,0,0,TDIUC,0,1,0,,Newly Curated,0,1
Semantic Video CNNs Through Representation Warping,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Gadde_Semantic_Video_CNNs_ICCV_2017_paper.html,0,1,CamVid,0,1,0,,,0,1
Semantic Video CNNs Through Representation Warping,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Gadde_Semantic_Video_CNNs_ICCV_2017_paper.html,0,1,CityScapes,0,1,0,,,,0
Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Hu_Attribute-Enhanced_Face_Recognition_ICCV_2017_paper.html,0,1,MultiPIE,0,1,0,,,0,1
Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Hu_Attribute-Enhanced_Face_Recognition_ICCV_2017_paper.html,0,1,CASIA NIR-VIR2.0 ,0,1,0,,,,0
Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Hu_Attribute-Enhanced_Face_Recognition_ICCV_2017_paper.html,0,1,LFW,0,1,0,,,,0
Low-Latency Video Semantic Segmentation,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Low-Latency_Video_Semantic_CVPR_2018_paper.html,0,0,Cityscapes,0,1,0,,,0,1
Low-Latency Video Semantic Segmentation,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Low-Latency_Video_Semantic_CVPR_2018_paper.html,0,0,CamVid,0,1,0,,,,0
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_An_Efficient_and_CVPR_2018_paper.html,0,0,Waveform (UCI),0,1,0,,,1,1
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_An_Efficient_and_CVPR_2018_paper.html,0,0,Spambase (UCI),0,1,0,,,,0
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_An_Efficient_and_CVPR_2018_paper.html,0,0,MNIST,0,1,0,,,,0
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Learning_Spatial-Temporal_Regularized_CVPR_2018_paper.html,0,1,OTB-2015,0,1,0,,,0,1
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Learning_Spatial-Temporal_Regularized_CVPR_2018_paper.html,0,1,Temple-Color,0,1,0,,,,0
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Learning_Spatial-Temporal_Regularized_CVPR_2018_paper.html,0,1,VOT-2016,0,1,0,,,,0
"Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects",CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Islam_Revisiting_Salient_Object_CVPR_2018_paper.html,0,0,PASCAL-S,0,1,0,,(modified w/ ground truth somehow),0,1
"Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects",CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Islam_Revisiting_Salient_Object_CVPR_2018_paper.html,0,0,PASCAL VOC,0,1,0,,,,0
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Learning_Deep_Models_CVPR_2018_paper.html,0,0,SiW,0,1,0,,Newly released,1,1
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Learning_Deep_Models_CVPR_2018_paper.html,0,0,Oulu-NPU,0,1,0,,,,0
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Learning_Deep_Models_CVPR_2018_paper.html,0,0,CASIA-MFSD,0,1,0,,,,0
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Learning_Deep_Models_CVPR_2018_paper.html,0,0,Replay-Attack,0,1,0,,,,0
Human Appearance Transfer,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Zanfir_Human_Appearance_Transfer_CVPR_2018_paper.html,0,0,Chictopia10k,0,1,0,,,1,1
Style Aggregated Network for Facial Landmark Detection,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Dong_Style_Aggregated_Network_CVPR_2018_paper.html,0,1,AFLW,0,1,0,,,0,1
Style Aggregated Network for Facial Landmark Detection,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Dong_Style_Aggregated_Network_CVPR_2018_paper.html,0,1,300-W,0,1,0,,,,0
Efficient Optimization for Rank-Based Loss Functions,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Mohapatra_Efficient_Optimization_for_CVPR_2018_paper.html,0,0,PASCAL VOC 2011,0,1,0,,,1,1
Efficient Optimization for Rank-Based Loss Functions,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Mohapatra_Efficient_Optimization_for_CVPR_2018_paper.html,0,0,PASCAL VOC 2007,0,1,0,,,,0
Efficient Optimization for Rank-Based Loss Functions,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Mohapatra_Efficient_Optimization_for_CVPR_2018_paper.html,0,0,CIFAR-10,0,1,0,,,,0
Two-Step Quantization for Low-Bit Neural Networks,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Two-Step_Quantization_for_CVPR_2018_paper.html,0,0,CIFAR-10,0,1,0,,,0,1
Two-Step Quantization for Low-Bit Neural Networks,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Two-Step_Quantization_for_CVPR_2018_paper.html,0,0,ILSVRC-12,0,1,0,,,,0
Deep Hashing via Discrepancy Minimization,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Deep_Hashing_via_CVPR_2018_paper.html,0,0,CIFAR-10,0,1,0,,,0,1
Deep Hashing via Discrepancy Minimization,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Deep_Hashing_via_CVPR_2018_paper.html,0,0,NUS-WIDE,0,1,0,,,,0
Deep Hashing via Discrepancy Minimization,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Deep_Hashing_via_CVPR_2018_paper.html,0,0,ImageNet,0,1,0,,,,0
Learning Single-View 3D Reconstruction with Limited Pose Supervision,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.html,0,1,ShapeNetCore,0,1,0,,,0,1
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.html,0,1,AFLW2000-3D,0,1,0,,,0,1
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.html,0,1,Florence,0,1,0,,,,0
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.html,0,1,AFLW-LFPA,0,1,0,,,,0
Joint Learning of Intrinsic Images and Semantic Segmentation,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Anil_Baslamisli_Joint_Learning_of_ECCV_2018_paper.html,0,1,Synthetic Dataset (public),0,1,0,,,1,1
"PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model",CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/George_Papandreou_PersonLab_Person_Pose_ECCV_2018_paper.html,0,0,COCO,0,1,0,,,0,1
Group Normalization,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yuxin_Wu_Group_Normalization_ECCV_2018_paper.html,0,1,ImageNet,0,1,0,,,0,1
Group Normalization,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yuxin_Wu_Group_Normalization_ECCV_2018_paper.html,0,1,COCO,0,1,0,,,,0
Group Normalization,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Yuxin_Wu_Group_Normalization_ECCV_2018_paper.html,0,1,Kinetics,0,1,0,,,,0
WildDash - Creating Hazard-Aware Benchmarks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Oliver_Zendel_WildDash_-_Creating_ECCV_2018_paper.html,0,0,WildDash,0,1,0,,(Newly Created),0,1
Learning Discriminative Video Representations Using Adversarial Perturbations,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Jue_Wang_Learning_Discriminative_Video_ECCV_2018_paper.html,0,0,HMDB-51,0,1,0,,,0,1
Learning Discriminative Video Representations Using Adversarial Perturbations,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Jue_Wang_Learning_Discriminative_Video_ECCV_2018_paper.html,0,0,NTU-RGBD,0,1,0,,,,0
Learning Discriminative Video Representations Using Adversarial Perturbations,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Jue_Wang_Learning_Discriminative_Video_ECCV_2018_paper.html,0,0,YUP++ dataset,0,1,0,,,,0
Face Recognition with Contrastive Convolution,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chunrui_Han_Face_Recognition_with_ECCV_2018_paper.html,0,0,LFW,0,1,0,,,0,1
Face Recognition with Contrastive Convolution,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chunrui_Han_Face_Recognition_with_ECCV_2018_paper.html,0,0,IJB-A,0,1,0,,,,0
Face Recognition with Contrastive Convolution,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Chunrui_Han_Face_Recognition_with_ECCV_2018_paper.html,0,0,CASIA-WebFace,0,1,0,,,,0
Quadtree Convolutional Neural Networks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pradeep_Kumar_Jayaraman_Quadtree_Convolutional_Neural_ECCV_2018_paper.html,0,0,MNIST,0,1,0,,,,1
Quadtree Convolutional Neural Networks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pradeep_Kumar_Jayaraman_Quadtree_Convolutional_Neural_ECCV_2018_paper.html,0,0,EMNIST Balanced,0,1,0,,,,0
Quadtree Convolutional Neural Networks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pradeep_Kumar_Jayaraman_Quadtree_Convolutional_Neural_ECCV_2018_paper.html,0,0,CASIA-HWDB1.1,0,1,0,,,,0
Quadtree Convolutional Neural Networks,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pradeep_Kumar_Jayaraman_Quadtree_Convolutional_Neural_ECCV_2018_paper.html,0,0,TU-Berlin Sketch Dataset,0,1,0,,,,0
Attend and Rectify: a gated attention mechanism for fine-grained recovery,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.html,0,1,CIFAR-10,0,1,0,,,0,1
Attend and Rectify: a gated attention mechanism for fine-grained recovery,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.html,0,1,Adience gender recognition task,0,1,0,,,,0
Attend and Rectify: a gated attention mechanism for fine-grained recovery,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.html,0,1,Stanford Dogs,0,1,0,,,,0
Attend and Rectify: a gated attention mechanism for fine-grained recovery,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.html,0,1,UEC Food-100,0,1,0,,,,0
Attend and Rectify: a gated attention mechanism for fine-grained recovery,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.html,0,1,CIFAR-100,0,1,0,,,,0
Depth Estimation Using Structured Light Flow -- Analysis of Projected Pattern Flow on an Object's Surface,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Furukawa_Depth_Estimation_Using_ICCV_2017_paper.html,0,0,Self-collected Planar,0,0,0,,(first proof of concept),0,1
Depth Estimation Using Structured Light Flow -- Analysis of Projected Pattern Flow on an Object's Surface,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Furukawa_Depth_Estimation_Using_ICCV_2017_paper.html,0,0,Self-collected Fast-moving Rounded Objects,0,0,0,,,,0
Predicting Deeper Into the Future of Semantic Segmentation,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Luc_Predicting_Deeper_Into_ICCV_2017_paper.html,0,0,Cityscapes,0,1,0,,,0,1
Predicting Deeper Into the Future of Semantic Segmentation,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Luc_Predicting_Deeper_Into_ICCV_2017_paper.html,0,0,CamVid,0,1,0,,,,0
"Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?",CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Behl_Bounding_Boxes_Segmentations_ICCV_2017_paper.html,0,1,KITTI 2015,0,1,0,,,0,1
"Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?",CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Behl_Bounding_Boxes_Segmentations_ICCV_2017_paper.html,0,1,Newly Annotated Stereo Image Dataset,0,1,0,,,,0
Video Frame Synthesis Using Deep Voxel Flow,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Video_Frame_Synthesis_ICCV_2017_paper.html,0,1,UCF-101,0,1,0,,,0,1
Video Frame Synthesis Using Deep Voxel Flow,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Video_Frame_Synthesis_ICCV_2017_paper.html,0,1,THUMOS-15,0,1,0,,,,0
Video Frame Synthesis Using Deep Voxel Flow,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Video_Frame_Synthesis_ICCV_2017_paper.html,0,1,KITTI 2012,0,1,0,,,,0
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,HMDB,0,1,0,,,0,1
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,JHMDB,0,1,0,,,,0
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,SHREC 3D Object Recognition Dataset,0,1,0,,,,0
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,KTH-TIPS2,0,1,0,,,,0
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,Brodatz Textures,0,1,0,,,,0
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,Virus Dataset,0,1,0,,,,0
Learning Discriminative ab-Divergences for Positive Definite Matrices,CV,ICCV,2017,http://openaccess.thecvf.com/content_ICCV_2017/papers/Cherian_Learning_Discriminative_ab-Divergences_ICCV_2017_paper.pdf,0,0,Cancer Dataset,0,1,0,,,,0
Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Tatarchenko_Octree_Generating_Networks_ICCV_2017_paper.html,0,1,ShapeNet-All,0,1,0,,,,1
Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Tatarchenko_Octree_Generating_Networks_ICCV_2017_paper.html,0,1,ShapeNet-Cars,0,1,0,,,,0
Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Tatarchenko_Octree_Generating_Networks_ICCV_2017_paper.html,0,1,BlendSwap,0,1,0,,,,0
Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html,0,1,Cityscapes,0,1,0,,,1,1
Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html,0,1,ImageNet,0,1,0,,,,0
Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html,0,1,Paintings by Classical Artists,0,1,0,,,,0
Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html,0,1,Flickr Photographs,0,1,0,,,,0
Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.html,0,1,DSLR Photographs,0,0,0,,Self collected?,,0
Representation Learning by Learning to Count,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Noroozi_Representation_Learning_by_ICCV_2017_paper.html,0,0,ImageNet,0,1,0,,,0,1
Representation Learning by Learning to Count,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Noroozi_Representation_Learning_by_ICCV_2017_paper.html,0,0,PASCAL VOC 2011,0,1,0,,,,0
Representation Learning by Learning to Count,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Noroozi_Representation_Learning_by_ICCV_2017_paper.html,0,0,PASCAL VOC 2007,0,1,0,,,,0
Representation Learning by Learning to Count,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Noroozi_Representation_Learning_by_ICCV_2017_paper.html,0,0,COCO,0,1,0,,,,0
Benchmarking Single-Image Reflection Removal Algorithms,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Wan_Benchmarking_Single-Image_Reflection_ICCV_2017_paper.html,0,1,SIR2,0,1,0,,Newly Created,0,1
Dense Non-Rigid Structure-From-Motion and Shading With Unknown Albedos,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Gallardo_Dense_Non-Rigid_Structure-From-Motion_ICCV_2017_paper.html,0,0,floral paper,0,1,0,,,1,1
Dense Non-Rigid Structure-From-Motion and Shading With Unknown Albedos,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Gallardo_Dense_Non-Rigid_Structure-From-Motion_ICCV_2017_paper.html,0,0,paper fortune teller,0,1,0,,,,0
Dense Non-Rigid Structure-From-Motion and Shading With Unknown Albedos,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Gallardo_Dense_Non-Rigid_Structure-From-Motion_ICCV_2017_paper.html,0,0,Kinetic Paper,0,1,0,,,,0
Dense Non-Rigid Structure-From-Motion and Shading With Unknown Albedos,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Gallardo_Dense_Non-Rigid_Structure-From-Motion_ICCV_2017_paper.html,0,0,Creased paper,0,0,0,,Newly Created,,0
Automatic Documentation of ICD Codes with Far-Field Speech Recognition,ML4H,ml4h2018,2018,,0,0,self-collected,0,0,0,,,1,1
Distinguishing correlation from causation using genome-wide association studies,ML4H,ml4h2018,2018,,0,0,GWAS summary association statistics,0,1,0,,,1,1
Prototypical Clustering Networks for Dermatological Disease Diagnosis,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.03066,0,0,Dermnet SkinDisease Atlas,0,1,0,http://www.dermnet.com/,,1,1
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network,ML4H,ml4h2018,2018,,0,0,CT volumes from 6648 unique participants enrolled in the NationalLung Screening Trial (NLST),0,1,0,https://wiki.cancerimagingarchive.net/display/NLST/National+Lung+Screening+Trial,,0,1
Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology,ML4H,ml4h2018,2018,,0,0,Camelyon 2016 challenge dataset,0,1,0,,,0,1
Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology,ML4H,ml4h2018,2018,,0,0,MNIST,0,1,0,,,0,0
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning,ML4H,ml4h2018,2018,,0,0,semi-synthetic dataset (TWINS) ,0,0,0,,,1,1
Unsupervised learning with contrastive latent variable models,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.06094,0,0,dataset of mice protein expression levels,0,0,0,,,0,1
Predicting pregnancy using large-scale data from a women's health tracking mobile application,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.02222,0,0,data from the Clue women’s health tracking app,1,0,0,,,0,1
DeepSPINE: automated lumbar spinal stenosis grading using deep learning,ML4H,ml4h2018,2018,https://arxiv.org/abs/1807.10215,0,0,collected at hospital,1,0,0,,,1,1
Compensated Integrated Gradients to Reliably Interpret EEG Classification,ML4H,ml4h2018,2018,,0,0,the PhysioNet polysomnography dataset,0,1,0,,,0,1
Compensated Integrated Gradients to Reliably Interpret EEG Classification,ML4H,ml4h2018,2018,,0,0,UCI EEG dataset,0,1,0,,,0,0
Compensated Integrated Gradients to Reliably Interpret EEG Classification,ML4H,ml4h2018,2018,,0,0,CHB-MIT Scalp EEG dataset ,0,1,0,,,0,0
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification,ML4H,ml4h2018,2018,,0,0,MIMIC-III,1,1,1,,,0,1
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes,ML4H,ml4h2018,2018,,0,0,Flatiron Health database,1,0,0,,,0,1
Automatic Diagnosis of Short-Duration 12-Lead ECG using Deep Convolutional Network,ML4H,ml4h2018,2018,,0,0,ECG recordings from Brazil,0,0,0,,,0,1
A Bayesian model of acquisition and clearance of bacterial colonization,ML4H,ml4h2018,2018,,0,0,Project CLEAR,0,0,0,,,1,1
A Probabilistic Model of Cardiac Physiology and Electrocardiograms,ML4H,ml4h2018,2018,,0,0,collected at hospital,1,0,0,,,1,1
A Probabilistic Model of Cardiac Physiology and Electrocardiograms,ML4H,ml4h2018,2018,,0,0,PTB Diagnostic ECG Database,0,1,0,,,1,0
Automated Radiation Therapy Treatment Planning using 3-D Generative Adversarial Networks,ML4H,ml4h2018,2018,https://arxiv.org/abs/1807.06489,0,1,collected,0,0,0,,,1,1
Imputation of Clinical Covariates in Time Series,ML4H,ml4h2018,2018,,0,0,Framingham Heart Study (FHS),1,1,1,https://www.framinghamheartstudy.org/fhs-for-researchers/research-application-overview/,,0,1
Advancing PICO Element Detection in Medical Text via Deep Neural Networks,ML4H,ml4h2018,2018,https://arxiv.org/abs/1810.12780,0,0,MEDLINE,0,1,0,,,0,1
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care,ML4H,ml4h2018,2018,,0,0,EHR from two hospitals,1,0,0,,,1,1
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks,ML4H,ml4h2018,2018,,0,0,collected,1,0,0,,,0,1
Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI,ML4H,ml4h2018,2018,,0,0,Predicting Language Outcome RecoveryAfter Stroke (PLORAS) database,1,1,1,https://www.ucl.ac.uk/ploras/,,0,1
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech,ML4H,ml4h2018,2018,,0,0,Dementia Bank,0,1,0,,,1,1
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech,ML4H,ml4h2018,2018,,0,0,Healthy Aging Picture Description(HAPD),0,0,0,,,1,0
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech,ML4H,ml4h2018,2018,,0,0,Healthy Aging Fluency & Paragraph tasks(HAFP),0,0,0,,,1,0
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech,ML4H,ml4h2018,2018,,0,0,Famous People,0,0,0,,,1,0
Corresponding Projections for Orphan Screening,ML4H,ml4h2018,2018,,0,1,BindingDB,0,1,0,,,1,1
Deep Learning with Attention to Predict Gestational Age of the Fetal Brain,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.07102,0,0,collected MRI,0,0,0,,,0,1
Rank Projection Trees for Multilevel Neural Network Interpretation,ML4H,ml4h2018,2018,,0,0,PanCancer Analysis of Whole Genomes dataset (PCAWG),0,1,0,,,1,1
Rank Projection Trees for Multilevel Neural Network Interpretation,ML4H,ml4h2018,2018,,0,0,PsychENCODE dataset,0,1,0,,,1,0
Using permutations to assess confounding in machine learning applications for digital health,ML4H,ml4h2018,2018,,0,0,collected,1,0,0,,,1,1
Robustness against the channel effect in pathological voice detection,ML4H,ml4h2018,2018,,0,0,collected,1,0,0,,,0,1
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01716,0,0,"AustralianImaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL) ",1,1,1,,,0,1
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies,ML4H,ml4h2018,2018,https://arxiv.org/abs/1812.01716,0,0,ADNI,1,1,1,,,0,0
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders,ML4H,ml4h2018,2018,https://arxiv.org/abs/1901.06618,0,0,K562 Cell Image Dataset,0,1,0,,,0,1
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders,ML4H,ml4h2018,2018,https://arxiv.org/abs/1901.06618,0,0,LIDC-IDRI dataset,0,1,0,,,0,0
Unsupervised Multimodal Representation Learning across Medical Images and Reports,ML4H,ml4h2018,2018,,0,0, MIMIC-CXR dataset,1,1,1,,,1,1
Deep Sequence Modeling for Hemorrhage Diagnosis,ML4H,ml4h2018,2018,,0,0,experiment,0,0,0,,,1,1
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records,ML4H,ml4h2018,2018,https://arxiv.org/abs/1811.08040,0,0,MIMIC-III,1,0,0,,,0,1
A Deep Generative Model of Vowel Formant Typology,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1004/n18-1004,0,0,"Becker-Kristal Corpus (""Predicting vowel inventories:
The dispersion-focalization theory revisited"")",0,1,0,,,0,1
Joint Bootstrapping Machines for High Confidence Relation Extraction,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1003/n18-1003,0,1,BREE Dataset,0,1,0,,,0,1
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1005/n18-1005,0,1,Indigenougs Mexican Morphological Segmentation Dataset,0,1,0,,Newly Released,0,1
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages,NLP,NAACL,2018,,0,1,"Nahuatl, Mexicanero corpus (Gutierrez-Vasques et al 2016)",0,1,0,,,,0
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages,NLP,NAACL,2018,,0,1,"Yorem, Nokki (Maldonado Martinez et al 2010)",0,1,0,,,,0
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis,NLP,ACL,2018,https://acl2018.org/paper/1606,0,0,KWDLC Evaluation Set,0,1,0,,,0,1
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis,NLP,ACL,2018,,0,0,Kyoto Corpus,0,1,0,,,,0
Joint Reasoning for Temporal and Causal Relations,NLP,ACL,2018,https://acl2018.org/paper/1095,0,1,Augmented Event Causality dataset,0,1,0,,Newly Released,0,1
Joint Reasoning for Temporal and Causal Relations,NLP,ACL,2018,,0,1,TimeBank-Dense,0,1,0,,,,0
Joint Reasoning for Temporal and Causal Relations,NLP,ACL,2018,,0,1,Causal-TimeBank,0,1,0,,,,0
Joint Reasoning for Temporal and Causal Relations,NLP,ACL,2018,,0,1,CaTeRs,0,1,0,,,,0
DeepPavlov: Open-Source Library for Dialogue Systems,NLP,ACL,2018,https://acl2018.org/paper/68-demo,0,1,DSTC2,0,1,0,,,1,1
DeepPavlov: Open-Source Library for Dialogue Systems,NLP,ACL,2018,,0,1,OntoNotes 5.0,0,1,0,,,,0
DeepPavlov: Open-Source Library for Dialogue Systems,NLP,ACL,2018,,0,1,SNIPS,0,1,0,,,,0
DeepPavlov: Open-Source Library for Dialogue Systems,NLP,ACL,2018,,0,1,SpellRuEval Dataset,0,1,0,,,,0
"Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference",NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1185/d18-1185,0,0,SciTail,0,1,0,,,0,1
"Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference",NLP,EMNLP,2018,,0,0,SNLI,0,1,0,,,,0
"Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference",NLP,EMNLP,2018,,0,0,MultiNLI,0,1,0,,,,0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1133/d18-1133,0,0,OntoNotes,0,1,0,,,1,1
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection,NLP,EMNLP,2018,,0,0,UIUC Dataset,0,1,0,,,,0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection,NLP,EMNLP,2018,,0,0,Bunescu and Huang Dataset,0,1,0,,,,0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection,NLP,EMNLP,2018,,0,0,TreeQA,0,1,0,,,,0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection,NLP,EMNLP,2018,,0,0,WikiQA,0,1,0,,,,0
A Neural Local Coherence Model for Text Quality Assessment,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1464/d18-1464,0,0,De Clercq 2014 Dataset,0,1,0,,,1,1
A Neural Local Coherence Model for Text Quality Assessment,NLP,EMNLP,2018,,0,0,ASAP Competition Dataset,0,1,0,,,,0
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1006,0,0,WMT-15,0,1,0,,,0,1
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1007,0,0,newstest-2013,0,1,0,,,,0
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1008,0,0,newstest-2015,0,1,0,,,,0
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1009,0,0,OpenSubtitle2016,0,1,0,,,,0
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information,NLP,ACL,2018,http://aclweb.org/anthology/N18-1007,0,1,Switchboard-NXT,0,1,0,,,1,1
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information,NLP,ACL,2018,http://aclweb.org/anthology/N18-1008,0,1,"Treebank, NXT release",0,1,0,,,,0
Tied Multitask Learning for Neural Speech Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1008,0,1," CALLHOME Spanish
Speech dataset (LDC2014T23)",0,1,0,,,0,1
Tied Multitask Learning for Neural Speech Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1009,0,1,Glossed Audio Corpus of Ainu Folklore,0,1,0,,,,0
Tied Multitask Learning for Neural Speech Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1010,0,1,Goddard 2017 corpus,0,1,0,,,,0
Tied Multitask Learning for Neural Speech Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1011,0,1,Mboshi-French corpus,0,1,0,,,,0
Tied Multitask Learning for Neural Speech Translation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1012,0,1, Europarl corpus,0,1,0,,,,0
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting,NLP,ACL,2018,http://aclweb.org/anthology/D18-1363,0,0,CoNLL–SIGMORPHON 2017,0,1,0,,,0,1
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning,NLP,ACL,2018,http://aclweb.org/anthology/D18-1090,0,0,ASAP dataset,0,1,0,,,0,1
Classifying Referential and Non-referential It Using Gaze,NLP,ACL,2018,http://aclweb.org/anthology/D18-1528,0,1,GECO corpus,0,1,0,,,0,1
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions,NLP,ACL,2018,http://aclweb.org/anthology/P18-3018,0,0,"Boyd-Graber 2018, Quiz Bowl",0,1,0,,,0,1
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions,NLP,ACL,2018,http://aclweb.org/anthology/P18-3019,0,0,"Jennings 2006, Quiz Bowl Questions",0,1,0,,validated and released the dataset,,0
Reinforced Extractive Summarization with Question-Focused Rewards,NLP,ACL,2018,http://aclweb.org/anthology/P18-3015,0,0,CNN dataset,0,1,0,,,0,1
End-to-End Reinforcement Learning for Automatic Taxonomy Induction,NLP,ACL,2018,http://aclweb.org/anthology/P18-1229,0,1,Bansal et al. 2014 WordNet taxonomies,0,1,0,,,0,1
End-to-End Reinforcement Learning for Automatic Taxonomy Induction,NLP,ACL,2018,http://aclweb.org/anthology/P18-1230,0,1, SemEval-2016 task 13 (TExEval2),0,1,0,,,,0
End-to-End Reinforcement Learning for Automatic Taxonomy Induction,NLP,ACL,2018,http://aclweb.org/anthology/P18-1231,0,1,Wikipedia dump,0,1,0,,,,0
End-to-End Reinforcement Learning for Automatic Taxonomy Induction,NLP,ACL,2018,http://aclweb.org/anthology/P18-1232,0,1,UMBC web-based corpus,0,1,0,,,,0
End-to-End Reinforcement Learning for Automatic Taxonomy Induction,NLP,ACL,2018,http://aclweb.org/anthology/P18-1233,0,1,One Billion Word Language Modeling Benchmark,0,1,0,,,,0
A Genre-Aware Attention Model to Improve the Likability Prediction of Books,NLP,ACL,2018,http://aclweb.org/anthology/D18-1375,0,1,Maharjan et al. 2017 book ratings,0,1,0,,,0,1
A Genre-Aware Attention Model to Improve the Likability Prediction of Books,NLP,ACL,2018,http://aclweb.org/anthology/D18-1376,0,1,Goodreads book covers,0,1,0,,,,0
Rapid Adaptation of Neural Machine Translation to New Languages,NLP,ACL,2018,http://aclweb.org/anthology/D18-1103,0,1,English TED corpus,0,1,0,,,0,1
Large-scale Exploration of Neural Relation Classification Architectures,NLP,ACL,2018,http://aclweb.org/anthology/D18-1250,0,1,SemEval,0,1,0,,,1,1
Large-scale Exploration of Neural Relation Classification Architectures,NLP,ACL,2018,http://aclweb.org/anthology/D18-1251,0,1,DDI-2013 corpus,0,1,0,,Biomedical,,0
Large-scale Exploration of Neural Relation Classification Architectures,NLP,ACL,2018,http://aclweb.org/anthology/D18-1252,0,1,CDR corpus,0,1,0,,Biomedical,,0
Large-scale Exploration of Neural Relation Classification Architectures,NLP,ACL,2018,http://aclweb.org/anthology/D18-1253,0,1,BB3 corpus,0,1,0,,Biomedical,,0
Large-scale Exploration of Neural Relation Classification Architectures,NLP,ACL,2018,http://aclweb.org/anthology/D18-1254,0,1,Phenebank corpus,0,1,0,,Biomedical,,0
Large-scale Exploration of Neural Relation Classification Architectures,NLP,ACL,2018,http://aclweb.org/anthology/D18-1255,0,1,ScienceIE corpus,0,1,0,,,,0
Stochastic Answer Networks for Machine Reading Comprehension,NLP,ACL,2018,http://aclweb.org/anthology/P18-1157,0,0,Stanford Question Answering Dataset (SQuAD),0,1,0,,,0,1
Stochastic Answer Networks for Machine Reading Comprehension,NLP,ACL,2018,http://aclweb.org/anthology/P18-1158,0,0,AddSent,0,1,0,,,,0
Stochastic Answer Networks for Machine Reading Comprehension,NLP,ACL,2018,http://aclweb.org/anthology/P18-1159,0,0,AddOneSent,0,1,0,,,,0
Stochastic Answer Networks for Machine Reading Comprehension,NLP,ACL,2018,http://aclweb.org/anthology/P18-1160,0,0,MS MARCO,0,1,0,,,,0
Sense-Aware Neural Models for Pun Location in Texts,NLP,ACL,2018,http://aclweb.org/anthology/P18-2087,0,0,SemEval 2017 Task 7,0,1,0,,,0,1
Exemplar Encoder-Decoder for Neural Conversation Generation,NLP,ACL,2018,http://aclweb.org/anthology/P18-1123,0,1,Ubuntu Dialogue Corpus,0,1,0,,,0,1
Exemplar Encoder-Decoder for Neural Conversation Generation,NLP,ACL,2018,http://aclweb.org/anthology/P18-1124,0,1,Tech Support Dataset,0,0,0,,,,0
Please Clap: Modeling Applause in Campaign Speeches,NLP,ACL,2018,http://aclweb.org/anthology/N18-1009,0,1,C-SPAN speech audio,0,1,0,,Created and released dataset,1,1
Attentive Interaction Model: Modeling Changes in View in Argumentation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1010,0,1,Tan et al. 2016 CMV subreddit text,0,1,0,,,0,1
"Automatic Focus Annotation: Bringing Formal Pragmatics Alive in
Analyzing the Information Structure of Authentic Data",NLP,ACL,2018,http://aclweb.org/anthology/N18-1011,0,0,German CREG corpus ,0,1,0,,,0,1
"Automatic Focus Annotation: Bringing Formal Pragmatics Alive in
Analyzing the Information Structure of Authentic Data",NLP,ACL,2018,http://aclweb.org/anthology/N18-1012,0,0,CREG-ExpertFocus,0,1,0,,,,0
"Automatic Focus Annotation: Bringing Formal Pragmatics Alive in
Analyzing the Information Structure of Authentic Data",NLP,ACL,2018,http://aclweb.org/anthology/N18-1013,0,0,CREG-5K,0,1,0,,,,0
"Interactive Instance-based Evaluation of
Knowledge Base Question Answering",NLP,ACL,2018,http://aclweb.org/anthology/D18-2020,0,1,Wikidata,0,1,0,,"Tool, no experiments",NA,1
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain,NLP,ACL,2018,http://aclweb.org/anthology/D18-1236,0,0,"Symbolic Aided Open Knowledge Expression
(SAOKE)",0,1,0,,,0,1
A Probabilistic Annotation Model for Crowdsourcing Coreference,NLP,ACL,2018,http://aclweb.org/anthology/D18-1218,0,0,Phrase Detectives corpus,0,1,0,,,1,1
A Probabilistic Annotation Model for Crowdsourcing Coreference,NLP,ACL,2018,http://aclweb.org/anthology/D18-1219,0,0,CONLL-2012 dataset,0,1,0,,,,0
Platforms for Non-Speakers Annotating Names in Any Language,NLP,ACL,2018,http://aclweb.org/anthology/P18-4001,0,0,Non-speaker name annotations for Ji et al. 2017 data,0,0,0,,,0,1
Platforms for Non-Speakers Annotating Names in Any Language,NLP,ACL,2018,http://aclweb.org/anthology/P18-4002,0,0, TAC-KBP EDL2017,0,1,0,,,,0
Platforms for Non-Speakers Annotating Names in Any Language,NLP,ACL,2018,http://aclweb.org/anthology/P18-4003,0,0,Pan et al. 2017 Wikipedia-based silver standard annotations,0,1,0,,,,0
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss,NLP,ACL,2018,http://aclweb.org/anthology/P18-1013,0,0,CNN/Daily Mail dataset,0,1,0,,,1,1
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss,NLP,ACL,2018,http://aclweb.org/anthology/P18-1014,0,0,Human evaluations via MTurk,0,0,0,,,,0
"NextGen AML: Distributed Deep Learning based Language Technologies
to Augment Anti Money Laundering Investigation",NLP,ACL,2018,http://aclweb.org/anthology/P18-4007,0,0,SemEval-2016 task 4,0,1,0,,,0,1
"NextGen AML: Distributed Deep Learning based Language Technologies
to Augment Anti Money Laundering Investigation",NLP,ACL,2018,http://aclweb.org/anthology/P18-4008,0,0,SemEval 2010 task 8,0,1,0,,,,0
"NextGen AML: Distributed Deep Learning based Language Technologies
to Augment Anti Money Laundering Investigation",NLP,ACL,2018,http://aclweb.org/anthology/P18-4009,0,0,New York Time data set,0,1,0,,,,0
"NextGen AML: Distributed Deep Learning based Language Technologies
to Augment Anti Money Laundering Investigation",NLP,ACL,2018,http://aclweb.org/anthology/P18-4010,0,0,Panama papers,0,1,0,,,,0
"NextGen AML: Distributed Deep Learning based Language Technologies
to Augment Anti Money Laundering Investigation",NLP,ACL,2018,http://aclweb.org/anthology/P18-4011,0,0,Paradise papers,0,1,0,,,,0
"NextGen AML: Distributed Deep Learning based Language Technologies
to Augment Anti Money Laundering Investigation",NLP,ACL,2018,http://aclweb.org/anthology/P18-4012,0,0,FATF publications,0,1,0,,,,0
"Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer",NLP,ACL,2018,http://aclweb.org/anthology/N18-1012,0,1,Grammarly’s Yahoo Answers Formality Corpus (GYAFC),0,1,0,,Created and released dataset,1,1
Using Large Ensembles of Control Variates for Variational Inference,General,NeurIPS,2018,https://papers.nips.cc/paper/8201-using-large-ensembles-of-control-variates-for-variational-inference.pdf,0,0,ionosphere,0,1,0,,,0,1
Using Large Ensembles of Control Variates for Variational Inference,General,NeurIPS,2018,https://papers.nips.cc/paper/8201-using-large-ensembles-of-control-variates-for-variational-inference.pdf,0,0,sonar,0,1,,,,0,0
Using Large Ensembles of Control Variates for Variational Inference,General,NeurIPS,2018,https://papers.nips.cc/paper/8201-using-large-ensembles-of-control-variates-for-variational-inference.pdf,0,0,australian,0,1,,,,0,0
Q-learning with Nearest Neighbors,General,NeurIPS,2018,https://papers.nips.cc/paper/7574-q-learning-with-nearest-neighbors.pdf,1,0,,,,,,assume MDP generated data,0,1
Scaling the Poisson GLM to massive neural datasets through polynomial approximations,General,NeurIPS,2018,https://papers.nips.cc/paper/7611-scaling-the-poisson-glm-to-massive-neural-datasets-through-polynomial-approximations.pdf,0,1,Retinal Ganglian cell data,0,1,0,https://www.ncbi.nlm.nih.gov/pubmed/15277596,,0,1
Online Learning with an Unknown Fairness Metric,General,NeurIPS,2018,https://papers.nips.cc/paper/7526-online-learning-with-an-unknown-fairness-metric.pdf,1,0,,,,,,,0,1
A Framework for the Quantitative Evaluation of Disentangled Representations,General,ICLR,2018,https://openreview.net/forum?id=By-7dz-AZ,0,1,Generated data,0,1,0,Monero in In ECCV Geometry Meets Deep Learning Workshop 2016,,0,1
A Simple Neural Attentive Meta-Learner,General,ICLR,2018,https://openreview.net/forum?id=B1DmUzWAW,0,0,Omniglot,0,1,0,,,1,1
A Simple Neural Attentive Meta-Learner,General,ICLR,2018,https://openreview.net/forum?id=B1DmUzWAW,0,0,mini-ImageNet,,,,,,1,0
A Simple Neural Attentive Meta-Learner,General,ICLR,2018,https://openreview.net/forum?id=B1DmUzWAW,0,0,generated data,,,,styled after Duan et al. (2016)),,1,0
A Simple Neural Attentive Meta-Learner,General,ICLR,2018,https://openreview.net/forum?id=B1DmUzWAW,0,0,simulated data,,,, Finn et al. (2017),,1,0
Non-Autoregressive Neural Machine Translation,General,ICLR,2018,https://openreview.net/forum?id=B1l8BtlCb,0,0,IWSLT16 En–De,0,1,0,,,0,1
Non-Autoregressive Neural Machine Translation,General,ICLR,2018,https://openreview.net/forum?id=B1l8BtlCb,0,0,WMT14 En–De,0,1,,,,0,0
Non-Autoregressive Neural Machine Translation,General,ICLR,2018,https://openreview.net/forum?id=B1l8BtlCb,0,0,WMT16 En–Ro,0,1,,,,0,0
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph,NLP,ACL,2018,http://aclweb.org/anthology/N18-1013,0,0,The Penn Discourse Treebank (PDTB),0,1,0,,,0,1
"A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence
Natural Language Generation",NLP,ACL,2018,http://aclweb.org/anthology/N18-1014,0,0,E2E restaurant dataset,0,1,0,,,0,1
"A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence
Natural Language Generation",NLP,ACL,2018,http://aclweb.org/anthology/N18-1015,0,0,TV dataset,0,1,0,,,0,0
"A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence
Natural Language Generation",NLP,ACL,2018,http://aclweb.org/anthology/N18-1016,0,0,Laptop dataset,0,1,0,,,0,0
Getting Gender Right in Neural Machine Translation,NLP,ACL,2018,http://aclweb.org/anthology/D18-1334,0,0,Tagged genders of speakers in Vanmassenhove 2018 dataset,0,0,0,,,0,1
Investigating Capsule Networks with Dynamic Routing for Text Classification,NLP,ACL,2018,http://aclweb.org/anthology/D18-1350,0,1,"movie reviews (MR) (Pang and Lee, 2005)",0,1,0,,,0,1
Investigating Capsule Networks with Dynamic Routing for Text Classification,NLP,ACL,2018,http://aclweb.org/anthology/D18-1351,0,1,Stanford Sentiment Treebankan extension of MR (SST-2),0,1,0,,,0,0
Investigating Capsule Networks with Dynamic Routing for Text Classification,NLP,ACL,2018,http://aclweb.org/anthology/D18-1352,0,1,Subjectivity dataset (Subj),0,1,0,,,0,0
Investigating Capsule Networks with Dynamic Routing for Text Classification,NLP,ACL,2018,http://aclweb.org/anthology/D18-1353,0,1,TREC question dataset (TREC),0,1,0,,,0,0
Investigating Capsule Networks with Dynamic Routing for Text Classification,NLP,ACL,2018,http://aclweb.org/anthology/D18-1354,0,1,customer review (CR),0,1,0,,,0,0
Investigating Capsule Networks with Dynamic Routing for Text Classification,NLP,ACL,2018,http://aclweb.org/anthology/D18-1355,0,1,AG’s news corpus,0,1,0,,,0,0
Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts,NLP,ACL,2018,http://aclweb.org/anthology/D18-1008,0,0,Annotated TAP frames on Penn Treebank WSJ Corpus,0,0,0,,"The text indicates the authors released the dataset, but can't find any reference.",0,1
"Extracting Commonsense Properties from Embeddings with Limited
Human Guidance",NLP,ACL,2018,http://aclweb.org/anthology/P18-2102,0,1,VERB PHYSICS data set,0,1,0,,,1,1
"Extracting Commonsense Properties from Embeddings with Limited
Human Guidance",NLP,ACL,2018,http://aclweb.org/anthology/P18-2103,0,1,PROPERTY COMMON SENSE,0,1,0,,Created and released dataset,1,0
"Extracting Commonsense Properties from Embeddings with Limited
Human Guidance",NLP,ACL,2018,http://aclweb.org/anthology/P18-2104,0,1,McRae Feature Norms dataset ,0,1,0,,,1,0
Training Classifiers with Natural Language Explanations,NLP,ACL,2018,http://aclweb.org/anthology/P18-1175,0,1,MTurk annotations of  Signal Media dataset,0,1,0,,Created and released dataset,0,1
Training Classifiers with Natural Language Explanations,NLP,ACL,2018,http://aclweb.org/anthology/P18-1176,0,1,MTurk annotations of 2015 BioCreative chemical-disease relation dataset,0,1,0,,Created and released dataset,0,0
Training Classifiers with Natural Language Explanations,NLP,ACL,2018,http://aclweb.org/anthology/P18-1177,0,1,MTurk annotations of biomedical literature,0,1,0,,Created and released dataset,0,0
"Strong Baselines for Neural Semi-Supervised Learning
under Domain Shift",NLP,ACL,2018,http://aclweb.org/anthology/P18-1096,0,1,SANCL 2012 shared task dataset,0,1,0,,,1,1
"Strong Baselines for Neural Semi-Supervised Learning
under Domain Shift",NLP,ACL,2018,http://aclweb.org/anthology/P18-1097,0,1,Ontonotes 4.0 release of the Penn treebank Wall Street Journal,0,1,0,,,1,0
"Strong Baselines for Neural Semi-Supervised Learning
under Domain Shift",NLP,ACL,2018,http://aclweb.org/anthology/P18-1098,0,1,Amazon reviews dataset,0,1,0,,,1,0
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design,General,ICLR,2018,https://openreview.net/forum?id=SywXXwJAb,0,0,MNIST,0,0,0,,,0,1
Concepts-Bridges: Uncovering Conceptual Bridges Based on Biomedical Concept Evolution,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/concepts-bridges-uncovering-conceptual-bridges-based-on-biomedical-concept-,0,1,MEDLINE,0,1,0,,,1,1
BagMinHash – Minwise Hashing Algorithm for Weighted Sets,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/bagminhash-minwise-hashing-algorithm-for-weighted-sets,0,1,set of nonnegative single-precision floating point numbers,0,1,0,,,1,1
Optimal Distributed Submodular Optimization via Sketching,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/optimal-distributed-submodular-optimization-via-sketching,0,0,gutenberg,0,1,0,https://www.gutenberg.org/ebooks/,,0,1
Optimal Distributed Submodular Optimization via Sketching,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/optimal-distributed-submodular-optimization-via-sketching,0,0,s-gutenberg,0,1,0,https://www.gutenberg.org/ebooks/,,0,0
Optimal Distributed Submodular Optimization via Sketching,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/optimal-distributed-submodular-optimization-via-sketching,0,0,reuters,0,1,0,,,0,0
Optimal Distributed Submodular Optimization via Sketching,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/optimal-distributed-submodular-optimization-via-sketching,0,0,wiki-main,0,1,0,,,0,0
Optimal Distributed Submodular Optimization via Sketching,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/optimal-distributed-submodular-optimization-via-sketching,0,0,wiki-talk,0,1,0,,,0,0
Optimal Distributed Submodular Optimization via Sketching,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/optimal-distributed-submodular-optimization-via-sketching,0,0,news20,0,1,0,,,0,0
Model-based Clustering of Short Text Streams,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/model-based-clustering-of-short-text-streams,0,1,Tweets,0,1,0,https://trec.nist.gov/data/microblog.html,,1,1
Model-based Clustering of Short Text Streams,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/model-based-clustering-of-short-text-streams,0,1,news20,0,1,,"Jianhua Yin and Jianyong Wang. 2014. A dirichlet multinomial mixture modelbased approach for short text clustering. In SIGKDD. ACM, 233–242",,1,0
A Melody-conditioned Lyrics Language Model,NLP,ACL,2018,http://aclweb.org/anthology/N18-1015,0,0,Melody-lyric pairs,0,1,0,,Created and released the dataset,1,1
Discourse-Aware Neural Rewards for Coherent Text Generation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1016,0,0,Now You’re Cooking dataset ,0,1,0,,,0,1
Natural Answer Generation with Heterogeneous Memory,NLP,ACL,2018,http://aclweb.org/anthology/N18-1017,0,0,WikiMovies-Synthetic,0,0,0,,,0,1
Natural Answer Generation with Heterogeneous Memory,NLP,ACL,2018,http://aclweb.org/anthology/N18-1018,0,0,WikiMovies-Wikipedia,0,0,0,,,0,0
Improving Abstraction in Text Summarization,NLP,ACL,2018,http://aclweb.org/anthology/D18-1207,0,0,CNN/Daily Mail dataset,0,1,0,,"Reported 95% CI for human scores, but not model scores",1,1
Improving Abstraction in Text Summarization,NLP,ACL,2018,http://aclweb.org/anthology/D18-1208,0,0, named entities from Hermann et al. (2015),0,1,0,,,1,0
"Hierarchical Neural Networks for Sequential Sentence Classification in
Medical Scientific Abstracts",NLP,ACL,2018,http://aclweb.org/anthology/D18-1349,0,1,NICTA-PIBOSO,0,1,0,,Biomedical,0,1
"Hierarchical Neural Networks for Sequential Sentence Classification in
Medical Scientific Abstracts",NLP,ACL,2018,http://aclweb.org/anthology/D18-1350,0,1,PubMed RCT ,0,1,0,,Biomedical,0,0
Modeling Localness for Self-Attention Networks,NLP,ACL,2018,http://aclweb.org/anthology/D18-1475,0,0,WMT17,0,1,0,,,0,1
Modeling Localness for Self-Attention Networks,NLP,ACL,2018,http://aclweb.org/anthology/D18-1476,0,0,newsdev2017,0,1,0,,,0,0
Modeling Localness for Self-Attention Networks,NLP,ACL,2018,http://aclweb.org/anthology/D18-1477,0,0,newstest2017,0,1,0,,,0,0
Modeling Localness for Self-Attention Networks,NLP,ACL,2018,http://aclweb.org/anthology/D18-1478,0,0,WMT14,0,1,0,,,0,0
Modeling Localness for Self-Attention Networks,NLP,ACL,2018,http://aclweb.org/anthology/D18-1479,0,0,newstest2013,0,1,0,,,0,0
Modeling Localness for Self-Attention Networks,NLP,ACL,2018,http://aclweb.org/anthology/D18-1480,0,0,newstest2014,0,1,0,,,0,0
Mitigating Adversarial Effects Through Randomization,General,ICLR,2018,https://openreview.net/forum?id=Sk9yuql0Z,0,1,ImageNet,0,1,0,,,0,1
Hierarchical Representations for Efficient Architecture Search,General,ICLR,2018,https://openreview.net/forum?id=BJQRKzbA-,0,0,CIFAR-10,0,1,,,,1,1
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments,General,ICLR,2018,https://openreview.net/forum?id=Sk2u1g-0-,0,1,RoboSumo,0,1,0,https://github.com/openai/robosumo,,1,1
Compressing Word Embeddings via Deep Compositional Code Learning,General,ICLR,2018,https://openreview.net/forum?id=BJRZzFlRb,0,1,f IMDB movie review dataset,0,1,0,,,0,1
Algorithms for Trip-Vehicle Assignment in Ride-Sharing,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16583,0,0,Generated,0,1,0,,sampled data,0,1
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16893,0,1,MNIST,0,1,0,,,0,1
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16894,0,1,CIFAR-10,0,1,0,,,0,0
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16895,0,1,ImageNet,0,1,0,,,0,0
Learning Differences between Visual Scanning Patterns Can Disambiguate Bipolar and Unipolar Patients,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16184,0,0,73 patients with Bipolar or Major Depressive Disorder,1,0,0,,Biomedical,0,1
Comparing Population Means under Local Differential Privacy: with Significance and Power,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16738,0,0,Unspecified real world data,0,0,0,,,1,1
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention,NLP,ACL,2018,http://aclweb.org/anthology/P18-2066,0,0,ACE2005 dataset,0,1,0,,,0,1
Chinese NER Using Lattice LSTM,NLP,ACL,2018,http://aclweb.org/anthology/P18-1144,0,1,OntoNotes,0,1,0,,,0,1
Chinese NER Using Lattice LSTM,NLP,ACL,2018,http://aclweb.org/anthology/P18-1145,0,1,MSRA,0,1,0,,,0,0
Chinese NER Using Lattice LSTM,NLP,ACL,2018,http://aclweb.org/anthology/P18-1146,0,1,Weibo NER,0,1,0,,,0,0
Chinese NER Using Lattice LSTM,NLP,ACL,2018,http://aclweb.org/anthology/P18-1147,0,1,Chinese resume dataset (annotated by authors),0,1,0,,,0,0
Finding Syntax in Human Encephalography with Beam Search,NLP,ACL,2018,http://aclweb.org/anthology/P18-1254,0,0,EEG dataset collected by authors,0,0,0,,"EEG data isn't PHI because it isn't collected by a ""covered entity""",1,1
Finding Syntax in Human Encephalography with Beam Search,NLP,ACL,2018,http://aclweb.org/anthology/P18-1255,0,0,Alice's Adventures in Wonderland audio,0,1,0,,,1,0
"Query and Output: Generating Words by Querying Distributed Word
Representations for Paraphrase Generation",NLP,ACL,2018,http://aclweb.org/anthology/N18-1018,0,1,Parallel Wikipedia Simplification Corpus (PWKP),0,1,0,,,0,1
"Query and Output: Generating Words by Querying Distributed Word
Representations for Paraphrase Generation",NLP,ACL,2018,http://aclweb.org/anthology/N18-1019,0,1,"English Wikipedia and Simple English
Wikipedia (EW-SEW)",0,1,0,,,0,0
"Query and Output: Generating Words by Querying Distributed Word
Representations for Paraphrase Generation",NLP,ACL,2018,http://aclweb.org/anthology/N18-1020,0,1,Large Scale Chinese Social Media Short Text Summarization Dataset (LCSTS):,0,1,0,,,0,0
"Simplification Using Paraphrases
and Context-based Lexical Substitution",NLP,ACL,2018,http://aclweb.org/anthology/N18-1019,0,1,MTurk annotations of Newsela corpus,0,1,0,,Created and released by authors.,0,1
"Simplification Using Paraphrases
and Context-based Lexical Substitution",NLP,ACL,2018,http://aclweb.org/anthology/N18-1020,0,1,WordNet,0,1,0,,,0,0
"Simplification Using Paraphrases
and Context-based Lexical Substitution",NLP,ACL,2018,http://aclweb.org/anthology/N18-1021,0,1,Paraphrase Database (PPDB),0,1,0,,,0,0
"Simplification Using Paraphrases
and Context-based Lexical Substitution",NLP,ACL,2018,http://aclweb.org/anthology/N18-1022,0,1,Simple Paraphrase Database (SimplePPDB),0,1,0,,,0,0
"Zero-Shot Question Generation from Knowledge Graphs for Unseen
Predicates and Entity Types",NLP,ACL,2018,http://aclweb.org/anthology/N18-1020,0,1,SimpleQuestions,0,1,0,,,1,1
"Zero-Shot Question Generation from Knowledge Graphs for Unseen
Predicates and Entity Types",NLP,ACL,2018,http://aclweb.org/anthology/N18-1021,0,1,FB5M subset of Freebase,0,1,0,,,1,0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning,General,ICML,2018,http://proceedings.mlr.press/v80/depeweg18a.html,0,0,Heteroscedastic (simulated),0,1,0,,generative process specified,1,1
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning,General,ICML,2018,http://proceedings.mlr.press/v80/depeweg18a.html,0,0,Bimodal (simulated),0,1,0,,generative process specified,1,0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning,General,ICML,2018,http://proceedings.mlr.press/v80/depeweg18a.html,0,0,Wet-Chicken,0,1,0,,,1,0
Large-Scale Cox Process Inference using Variational Fourier Features,General,ICML,2018,http://proceedings.mlr.press/v80/john18a.html,0,0,Synthetic,0,0,0,,,1,1
Large-Scale Cox Process Inference using Variational Fourier Features,General,ICML,2018,http://proceedings.mlr.press/v80/john18a.html,0,0,Porto taxi trajectory dataset,0,1,0,,,1,0
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction,General,ICML,2018,http://proceedings.mlr.press/v80/brukhim18a.html,0,0,Bibtex,0,1,0,,Tests on NLP task,0,1
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction,General,ICML,2018,http://proceedings.mlr.press/v80/brukhim18a.html,0,0,Delicious,0,1,0,,,0,0
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction,General,ICML,2018,http://proceedings.mlr.press/v80/brukhim18a.html,0,0,Bookmarks,0,1,0,,,0,0
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration,General,ICML,2018,http://proceedings.mlr.press/v80/poon18a.html,0,1,LIBSVM (mushrooms),0,1,0,,,N/A,1
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration,General,ICML,2018,http://proceedings.mlr.press/v80/poon18a.html,0,1,LIBSVM (rcv1.binary),0,1,0,,,N/A,0
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17286,0,1,Lakh Pianoroll Dataset,0,1,0,,Newly Released,N/A,1
"Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication",General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17356,0,0,Self-collected,0,0,0,,,0,1
Learning Deep Structured Active Contours End-to-End,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Marcos_Learning_Deep_Structured_CVPR_2018_paper.html,0,1,Vaihingen,0,1,0,,,0,1
Learning Deep Structured Active Contours End-to-End,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Marcos_Learning_Deep_Structured_CVPR_2018_paper.html,0,1,Bing Huts,0,1,0,,,0,0
Learning Deep Structured Active Contours End-to-End,CV,CVPR,2018,http://openaccess.thecvf.com/content_cvpr_2018/html/Marcos_Learning_Deep_Structured_CVPR_2018_paper.html,0,1,TorontoCity,0,1,0,,,0,0
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16523,0,0,ProPublica COMPAS dataset,0,1,0,,,0,1
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16524,0,0,SQF Dataset,0,1,0,,,0,0
Distributed Composite Quantization,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16470,0,0,MNIST,0,1,0,,,0,1
Distributed Composite Quantization,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16471,0,0,LabelMe22K,0,1,0,,,0,0
Distributed Composite Quantization,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16472,0,0,INRIA Holidays,0,1,0,,,0,0
Distributed Composite Quantization,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16473,0,0,UK Bench,0,1,0,,,0,0
Distributed Composite Quantization,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16474,0,0,SIFT1M,0,1,0,,,0,0
Distributed Composite Quantization,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16475,0,0,MIRFlickr1M,0,1,0,,,0,0
Resolving Abstract Anaphora in Conversational Assistants using a Hierarchically-stacked RNN,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/resolving-abstract-anaphora-in-conversational-assistants-using-a-hierarchic,0,0,Leave,0,1,0,,,1,1
Resolving Abstract Anaphora in Conversational Assistants using a Hierarchically-stacked RNN,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/resolving-abstract-anaphora-in-conversational-assistants-using-a-hierarchic,0,0,TCS Public Leave,0,1,0,,,1,0
Resolving Abstract Anaphora in Conversational Assistants using a Hierarchically-stacked RNN,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/resolving-abstract-anaphora-in-conversational-assistants-using-a-hierarchic,0,0,Yelp '13,0,1,0,,,1,0
Leveraging Meta-path based Context for Top N recommendation with Co-attention mechanism,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/leveraging-meta-path-based-context-for-top-n-recommendation-with-co-attenti,0,0,MovieLens,0,1,0,,,1,1
Leveraging Meta-path based Context for Top N recommendation with Co-attention mechanism,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/leveraging-meta-path-based-context-for-top-n-recommendation-with-co-attenti,0,0,LastFM,0,1,0,,,1,0
Leveraging Meta-path based Context for Top N recommendation with Co-attention mechanism,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/leveraging-meta-path-based-context-for-top-n-recommendation-with-co-attenti,0,0,Yelp,0,1,0,,,1,0
Analyzing Uncertainty in Neural Machine Translation,"General, NLP",ICML,2018,http://proceedings.mlr.press/v80/ott18a.html,0,1,WMT’14 English-German (En-De),0,1,0,,,0,1
Analyzing Uncertainty in Neural Machine Translation,"General, NLP",ICML,2018,http://proceedings.mlr.press/v80/ott18a.html,0,1,WMT’17 English-German (En-De),0,1,0,,,0,0
Analyzing Uncertainty in Neural Machine Translation,"General, NLP",ICML,2018,http://proceedings.mlr.press/v80/ott18a.html,0,1,WMT’14 English-French (En-Fr),0,1,0,,,0,0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning,General,ICML,2018,http://proceedings.mlr.press/v80/fruit18a.html,1,1,,,,,,,1,1
Learning to Share and Hide Intentions using Information Regularization,General,NeurIPS,2018,https://papers.nips.cc/paper/8227-learning-to-share-and-hide-intentions-using-information-regularization.pdf,0,1,Grid World (simulated),0,1,0,,,1,1
Learning to Share and Hide Intentions using Information Regularization,General,NeurIPS,2018,https://papers.nips.cc/paper/8227-learning-to-share-and-hide-intentions-using-information-regularization.pdf,0,1,Key-and-door (simulated),0,1,0,,,1,0
L4: Practical loss-based stepsize adaptation for deep learning,General,NeurIPS,2018,https://papers.nips.cc/paper/7879-l4-practical-loss-based-stepsize-adaptation-for-deep-learning.pdf,0,0,Synthetic,0,0,0,,,1,1
L4: Practical loss-based stepsize adaptation for deep learning,General,NeurIPS,2018,https://papers.nips.cc/paper/7879-l4-practical-loss-based-stepsize-adaptation-for-deep-learning.pdf,0,0,MNIST,0,1,0,,,1,0
L4: Practical loss-based stepsize adaptation for deep learning,General,NeurIPS,2018,https://papers.nips.cc/paper/7879-l4-practical-loss-based-stepsize-adaptation-for-deep-learning.pdf,0,0,CIFAR-10,0,1,0,,,1,0
L4: Practical loss-based stepsize adaptation for deep learning,General,NeurIPS,2018,https://papers.nips.cc/paper/7879-l4-practical-loss-based-stepsize-adaptation-for-deep-learning.pdf,0,0,Fashion MNIST,0,1,0,,,1,0
L4: Practical loss-based stepsize adaptation for deep learning,General,NeurIPS,2018,https://papers.nips.cc/paper/7879-l4-practical-loss-based-stepsize-adaptation-for-deep-learning.pdf,0,0,DNC,0,1,0,,,1,0
Automated Essay Scoring in the Presence of Biased Ratings,NLP,ACL,2018,http://aclweb.org/anthology/N18-1021,0,0,Essay Corpus,0,1,0,,Newly Released,1,1
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora,NLP,ACL,2018,https://acl2018.org/paper/757,0,1,BLESS,0,1,0,,,0,1
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora,NLP,ACL,2018,https://acl2018.org/paper/758,0,1,LEDS,0,1,0,,,0,0
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora,NLP,ACL,2018,https://acl2018.org/paper/759,0,1,EVAL,0,1,0,,,0,0
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora,NLP,ACL,2018,https://acl2018.org/paper/760,0,1,SHWARTZ,0,1,0,,,0,0
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora,NLP,ACL,2018,https://acl2018.org/paper/761,0,1,WBLESS,0,1,0,,,0,0
Synthetic Data Made to Order: The Case of Parsing,NLP,ACL,2018,http://aclweb.org/anthology/D18-1163,0,1,Universal Dependencies v1.2,0,1,0,,,0,1
Non-Markovian Globally Consistent Multi-Object Tracking,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.html,0,1,Duke,0,1,0,,,0,1
Non-Markovian Globally Consistent Multi-Object Tracking,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.html,0,1,Town,0,1,0,,,0,0
Non-Markovian Globally Consistent Multi-Object Tracking,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.html,0,1,Hotel,0,1,0,,,0,0
Non-Markovian Globally Consistent Multi-Object Tracking,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.html,0,1,Station,0,1,0,,,0,0
Non-Markovian Globally Consistent Multi-Object Tracking,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.html,0,1,ETH,0,1,0,,,0,0
Non-Markovian Globally Consistent Multi-Object Tracking,CV,ICCV,2017,http://openaccess.thecvf.com/content_iccv_2017/html/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.html,0,1,Rene,0,1,0,,,0,0
Visual Question Answering as a Meta Learning Task,CV,ECCV,2018,http://openaccess.thecvf.com/content_ECCV_2018/html/Damien_Teney_Visual_Question_Answering_ECCV_2018_paper.html,0,0,VQA v2,0,1,0,,,0,1
Certified Defenses against Adversarial Examples,General,ICLR,2018,https://openreview.net/forum?id=Bys4ob-Rb,0,1,MNIS,0,1,0,,,0,1
Large scale distributed neural network training through online distillation,General,ICLR,2018,https://openreview.net/forum?id=rkr1UDeC-,0,0,Common Crawl,0,1,0,,Newly Released,1,1
Large scale distributed neural network training through online distillation,General,ICLR,2018,https://openreview.net/forum?id=rkr1UDeC-,0,0,ImageNet,0,1,0,,,1,0
Large scale distributed neural network training through online distillation,General,ICLR,2018,https://openreview.net/forum?id=rkr1UDeC-,0,0,Criteo Display Ad Challenge,0,1,0,,,1,0
ACTIVATION MAXIMIZATION GENERATIVE ADVERSARIAL NETS,General,ICLR,2018,https://openreview.net/forum?id=HyyP33gAZ,0,1,CIFAR-10,0,1,0,,,1,1
ACTIVATION MAXIMIZATION GENERATIVE ADVERSARIAL NETS,General,ICLR,2018,https://openreview.net/forum?id=HyyP33gAZ,0,1,TINY-IMAGENET,0,1,0,,,1,0
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting,General,ICLR,2018,https://openreview.net/forum?id=SJiHXGWAZ,0,1,METR-LA,0,1,0,,,1,1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting,General,ICLR,2018,https://openreview.net/forum?id=SJiHXGWAZ,0,1,PEMS-BAY,0,1,0,,,1,0
Unsupervised Neural Machine Translation,General,ICLR,2018,https://openreview.net/forum?id=Sy2ogebAW,0,1,WMT 2014 shared task,0,1,0,,,0,1
Densely Connected Attention Propagation for Reading Comprehension,General,NeurIPS,2018,https://papers.nips.cc/paper/7739-densely-connected-attention-propagation-for-reading-comprehension.pdf,0,0,NewsQA,0,1,0,,,0,1
Densely Connected Attention Propagation for Reading Comprehension,General,NeurIPS,2018,https://papers.nips.cc/paper/7739-densely-connected-attention-propagation-for-reading-comprehension.pdf,0,0,SearchQA,0,1,0,,,0,0
Densely Connected Attention Propagation for Reading Comprehension,General,NeurIPS,2018,https://papers.nips.cc/paper/7739-densely-connected-attention-propagation-for-reading-comprehension.pdf,0,0,NarrativeQA,0,1,0,,,1,0
Densely Connected Attention Propagation for Reading Comprehension,General,NeurIPS,2018,https://papers.nips.cc/paper/7739-densely-connected-attention-propagation-for-reading-comprehension.pdf,0,0,Quasar-T,,,,,,,0
Content-Based Citation Recommendation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1022,0,1,DBLP,0,1,0,,,1,1
Content-Based Citation Recommendation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1023,0,1,OpenCorpus,,,,,,,0
Content-Based Citation Recommendation,NLP,ACL,2018,http://aclweb.org/anthology/N18-1024,0,1,PubMed,,,,,,,0
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation,General,NeurIPS,2018,https://papers.nips.cc/paper/7607-hybrid-mst-a-hybrid-active-sampling-strategy-for-pairwise-preference-aggregation.pdf,0,1,Video Quality Assessment(VQA),0,1,0,,,0,1
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation,General,NeurIPS,2018,https://papers.nips.cc/paper/7607-hybrid-mst-a-hybrid-active-sampling-strategy-for-pairwise-preference-aggregation.pdf,0,1,Image Quality Assessment (IQA),0,1,0,,,0,0
Adversarially Robust Optimization with Gaussian Processes,General,NeurIPS,2018,https://papers.nips.cc/paper/7818-adversarially-robust-optimization-with-gaussian-processes.pdf,0,1,MovieLens-100K,0,1,0,,,1,1
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images,General,NeurIPS,2018,https://papers.nips.cc/paper/8061-deep-network-for-the-integrated-3d-sensing-of-multiple-people-in-natural-images.pdf,0,0,Human3.6m,0,1,0,,,0,1
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images,General,NeurIPS,2018,https://papers.nips.cc/paper/8061-deep-network-for-the-integrated-3d-sensing-of-multiple-people-in-natural-images.pdf,0,0,CMU Panoptic,0,1,0,,,0,0
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups,General,ICML,2018,http://proceedings.mlr.press/v80/kondor18a.html,1,0,,,,,,Theoretical Statistics ,,1
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1023/n18-1023,0,1,MultiRC,0,1,0,,Dataset paper,0,1
"Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres",General,NeurIPS,2018,https://papers.nips.cc/paper/7589-deep-complex-invertible-networks-for-inversion-of-transmission-effects-in-multimode-optical-fibres.pdf,0,1,Optical fibre inverse problem Benchmark collection,0,1,0,,released with the paper,0,1
Neural Architecture Optimization,General,NeurIPS,2018,https://papers.nips.cc/paper/8007-neural-architecture-optimization.pdf,0,1,CIFAR-10/100,0,1,0,,,0,1
Neural Architecture Optimization,General,NeurIPS,2018,https://papers.nips.cc/paper/8007-neural-architecture-optimization.pdf,0,1,PTB,0,1,0,,,0,0
Neural Architecture Optimization,General,NeurIPS,2018,https://papers.nips.cc/paper/8007-neural-architecture-optimization.pdf,0,1,WikiText-2,0,1,0,,,0,0
"Zero-shot Sequence Labeling:
Transferring Knowledge from Sentences to Tokens",NLP,ACL,2018,http://aclweb.org/anthology/N18-1027,0,1,Bioscope,1,1,1,,"Bioscope has PI in med, its unclear if Med was used",,1
"Zero-shot Sequence Labeling:
Transferring Knowledge from Sentences to Tokens",NLP,ACL,2018,http://aclweb.org/anthology/N18-1028,0,1,FCE,1,1,,,,,0
"Zero-shot Sequence Labeling:
Transferring Knowledge from Sentences to Tokens",NLP,ACL,2018,http://aclweb.org/anthology/N18-1029,0,1,SemEval Sentiment Detection in Twitter,1,1,,,,,0
PixelSNAIL: An Improved Autoregressive Generative Model,General,ICML,2018,http://proceedings.mlr.press/v80/chen18h.html,0,1,CIFAR-10,0,1,0,,,0,1
PixelSNAIL: An Improved Autoregressive Generative Model,General,ICML,2018,http://proceedings.mlr.press/v80/chen18h.html,0,1,32×32 ImageNet,0,1,0,,,0,0
PixelSNAIL: An Improved Autoregressive Generative Model,General,ICML,2018,http://proceedings.mlr.press/v80/chen18h.html,0,1,64×64 ImageNet,0,1,0,,,0,0
Transformation Autoregressive Networks,General,ICML,2018,http://proceedings.mlr.press/v80/oliva18a.html,0,1,power,0,1,0,,,1,1
Transformation Autoregressive Networks,General,ICML,2018,http://proceedings.mlr.press/v80/oliva18a.html,0,1,gas,0,1,0,,,1,0
Transformation Autoregressive Networks,General,ICML,2018,http://proceedings.mlr.press/v80/oliva18a.html,0,1,hepmass,0,1,0,,,1,0
Transformation Autoregressive Networks,General,ICML,2018,http://proceedings.mlr.press/v80/oliva18a.html,0,1,minibone,0,1,0,,,1,0
Transformation Autoregressive Networks,General,ICML,2018,http://proceedings.mlr.press/v80/oliva18a.html,0,1,BSDS300,0,1,0,,,1,0
Tensorized Projection for High-Dimensional Binary Embedding,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16380,0,1,MIRFLICKR-1M,0,1,0,,,0,1
Tensorized Projection for High-Dimensional Binary Embedding,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16381,0,1,Holidays + MIRFlickr-1M,0,1,0,,,0,0
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors,General,ICML,2018,http://proceedings.mlr.press/v80/ghosh18a.html,0,0,UCI datasets,0,1,0,,"Some of those are Med, but mostly numerical",,1
Predicting Aesthetic Score Distribution Through Cumulative Jensen-Shannon Divergence,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16074,0,0,AVA,0,1,0,,,0,1
Norm Conflict Resolution in Stochastic Domains,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16488,1,0,,,,,,,,1
Deep Representation-Decoupling Neural Networks for Monaural Music Mixture Separation,General,AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16733/15675,0,0,DSD100,0,1,0,,,0,1
Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach,"General, ML4H",AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16199/15676,0,0,MarketScan Commercial Claims and Encounter,1,0,0,,from Truven Health,,1
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks,NLP,ACL,2018,https://acl2018.org/paper/1038,0,1,Twitter15,0,1,0,,,0,1
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks,NLP,ACL,2018,https://acl2018.org/paper/1039,0,1,Twitter16,0,1,0,,,,0
"
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction","General, ML4H",AAAI,2018,https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17085/15677,0,0,MIMIC-III,1,1,1,,,1,1
Triangular Architecture for Rare Language Translation,NLP,ACL,2018,https://acl2018.org/paper/79,0,0,MultiUN,0,1,0,,,0,1
Triangular Architecture for Rare Language Translation,NLP,ACL,2018,https://acl2018.org/paper/80,0,0,IWSLT2012,,,,,,,0
A Neural Architecture for Automated ICD Coding,"NLP, ML4H",ACL,2018,https://acl2018.org/paper/228,0,,MIMIC-III,1,1,1,,,0,1
"Ultra-Fine Entity Typing
",NLP,ACL,2018,https://acl2018.org/paper/880,0,1,custom dataset,0,1,0,,,,1
Coherence-Aware Neural Topic Modeling,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1096/d18-1096,0,0,DailyKOS,0,1,0,,,0,1
Coherence-Aware Neural Topic Modeling,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1096/d18-1097,0,0,NIPS,0,1,0,,,,0
Coherence-Aware Neural Topic Modeling,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1096/d18-1098,0,0,NYTimes,0,1,0,,,,0
"Fluency Boost Learning and Inference for
Neural Grammatical Error Correction",NLP,ACL,2018,http://aclweb.org/anthology/P18-1097,0,0,Lang-8 Corpus,0,1,0,,,1,1
"Fluency Boost Learning and Inference for
Neural Grammatical Error Correction",NLP,ACL,2018,http://aclweb.org/anthology/P18-1098,0,0,CLC,0,1,0,,,,0
"Fluency Boost Learning and Inference for
Neural Grammatical Error Correction",NLP,ACL,2018,http://aclweb.org/anthology/P18-1099,0,0,NUCLE,0,1,0,,,,0
"Fluency Boost Learning and Inference for
Neural Grammatical Error Correction",NLP,ACL,2018,http://aclweb.org/anthology/P18-1100,0,0,CoNLL2014,0,1,0,,,,0
"Fluency Boost Learning and Inference for
Neural Grammatical Error Correction",NLP,ACL,2018,http://aclweb.org/anthology/P18-1101,0,0,JFLEG,0,1,0,,,,0
Neural Argument Generation Augmented with Externally Retrieved Evidence,NLP,ACL,2018,https://acl2018.org/paper/961,0,1,Reddit Topic Dataset,0,1,0,http://xinyuhua.github.io/Resources/,Newly collected,1,1
An Empirical Study of Building a Strong Baseline for Constituency Parsing,NLP,ACL,2018,https://acl2018.org/paper/1501,0,1,PTB Dataset,0,1,0,,,1,1
Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach,NLP,ACL,2018,https://acl2018.org/paper/844,0,1,WSJ Dataset,0,1,0,,Newly collected,1,1
Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach,NLP,ACL,2018,https://acl2018.org/paper/845,0,1,CNET Dataset,0,1,0,,,,0
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation,NLP,ACL,2018,https://acl2018.org/paper/1296,0,0,CNN/DailyMail,0,1,0,,,1,1
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation,NLP,ACL,2018,https://acl2018.org/paper/1297,0,0,Gigaword,0,1,0,,,,0
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation,NLP,ACL,2018,https://acl2018.org/paper/1298,0,0,DUC-2002 Transfer,0,1,0,,,,0
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation,NLP,ACL,2018,https://acl2018.org/paper/1299,0,0,SQuAD,0,1,0,,,,0
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation,NLP,ACL,2018,https://acl2018.org/paper/1300,0,0,SNLI Classification,0,1,0,,,,0
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays,NLP,ACL,2018,https://acl2018.org/paper/1626,0,0,Essay Argument Persuasiveness Dataset,0,1,0,,Newly released,0,1
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1024/n18-1024,0,1,ASAP Dataset,0,1,0,,,1,1
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1024/n18-1025,0,1,Synthetic Dataset,0,1,0,,,,0
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1026/n18-1026,0,0,AOL Query Log,0,1,0,,,1,1
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1026/n18-1027,0,0,ClueWeb12B,0,1,0,,,,0
QuickEdit: Editing Text & Translations by Crossing Words Out,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1025/n18-1025,0,0,IWSLT’14 De-En,0,1,0,,,0,1
QuickEdit: Editing Text & Translations by Crossing Words Out,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1025/n18-1026,0,0,WMT'14 De-En,0,1,0,,,,0
QuickEdit: Editing Text & Translations by Crossing Words Out,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1025/n18-1027,0,0,WMT'14 En-Fr,0,1,0,,,,0
QuickEdit: Editing Text & Translations by Crossing Words Out,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1025/n18-1028,0,0,MTC Dataset,0,1,0,,,,0
Algorithms for Hiring and Outsourcing in the Online Labor Market,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/algorithms-for-hiring-and-outsourcing-in-the-online-labor-market,0,1,UpWork,0,1,0,,Newly released,0,1
Algorithms for Hiring and Outsourcing in the Online Labor Market,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/algorithms-for-hiring-and-outsourcing-in-the-online-labor-market,0,1,Freelancer,0,1,0,,Newly released,,0
Algorithms for Hiring and Outsourcing in the Online Labor Market,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/algorithms-for-hiring-and-outsourcing-in-the-online-labor-market,0,1,Guru,0,1,0,,Newly released,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,pendigits,0,1,0,,All from Lib-SVM,0,1
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,letter,0,1,0,,,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,mnist,0,1,0,,,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,acoustic,0,1,0,,,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,ijcnn1,0,1,0,,,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,cod_rna,0,1,0,,,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,covtype-mult,0,1,0,,,,0
Scalable Spectral Clustering Using Random Binning Features,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-spectral-clustering-using-random-binning-features,0,0,poker,0,1,0,,,,0
Training Big Random Forests with Little Resources,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/training-big-random-forests-with-little-resources,0,1,covtype,0,1,0,,UCI,0,1
Training Big Random Forests with Little Resources,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/training-big-random-forests-with-little-resources,0,1,susy,0,1,0,,UCI,,0
Training Big Random Forests with Little Resources,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/training-big-random-forests-with-little-resources,0,1,higgs,0,1,0,,UCI,,0
Training Big Random Forests with Little Resources,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/training-big-random-forests-with-little-resources,0,1,landsat-osm,0,1,0,,UCI,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,Alphadigits,0,1,0,,,0,1
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,Semeion,0,1,0,,,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,USPS,0,1,0,,,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,ISOLET,0,1,0,,,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,Letter,0,1,0,,,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,MNIST,0,1,0,,,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,ImageNet,0,1,0,,,,0
Scalable Active Learning by Approximated Error Reduction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/scalable-active-learning-by-approximated-error-reduction,0,1,MNISTsM,0,1,0,,,,0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/easing-embedding-learning-by-comprehensive-transcription-of-heterogeneous-i,0,1,DBLP,0,1,0,,,0,1
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/easing-embedding-learning-by-comprehensive-transcription-of-heterogeneous-i,0,1,YAGO,0,1,0,,,,0
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction,General,KDD,2018,https://www.kdd.org/kdd2018/accepted-papers/view/exploring-student-check-in-behavior-for-improved-point-of-interest-predicti,0,0,PurDue Check-in Data,0,0,0,,Newly created,0,1
Dynamic Neural Program Embeddings for Program Repair,General,ICLR,2018,https://openreview.net/forum?id=BJuWrGW0Z,0,0,CodeHunt Platform Dataset,0,0,0,,,0,1
Dynamic Neural Program Embeddings for Program Repair,General,ICLR,2018,https://openreview.net/forum?id=BJuWrGW0Z,0,0,Synthetic Dataset,0,0,0,,,,0
Alternating Randomized Block Coordinate Descent,General,ICML,2018,http://proceedings.mlr.press/v80/diakonikolas18a.html,0,0,BlogFeedback,0,1,0,,UCI,0,1
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations,General,ICML,2018,http://proceedings.mlr.press/v80/lu18d.html,0,0,CIFAR-10,0,1,0,,,0,1
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations,General,ICML,2018,http://proceedings.mlr.press/v80/lu18d.html,0,0,CIFAR-100,0,1,0,,,,0
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations,General,ICML,2018,http://proceedings.mlr.press/v80/lu18d.html,0,0,ImageNet,0,1,0,,,,0
Variable Typing: Assigning Meaning to Variables in Mathematical Text,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1028/n18-1028,0,0,Arxiv Variable Typing,0,1,0,,Newly created,1,1
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1029/n18-1029,0,0,20 Newsgroups,0,1,0,,,0,1
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1029/n18-1030,0,0,DBPedia,0,1,0,,,,0
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1029/n18-1031,0,0,SNLI,0,1,0,,,,0
Comparing Constraints for Taxonomic Organization,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1030/n18-1030,0,0,BLESS,0,1,0,,,0,1
Comparing Constraints for Taxonomic Organization,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1030/n18-1031,0,0,ROOT09,0,1,0,,,,0
Comparing Constraints for Taxonomic Organization,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1030/n18-1032,0,0,EVALution,0,1,0,,,,0
Comparing Constraints for Taxonomic Organization,NLP,NAACL,2018,https://aclanthology.info/papers/N18-1030/n18-1033,0,0,K&H+N,0,1,0,,,0,0
http://aclweb.org/anthology/D18-1019,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1019/d18-1019,0,1,ACE-2004,0,1,0,,,,1
http://aclweb.org/anthology/D18-1019,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1019/d18-1019,0,1,ACE-2005,0,1,0,,,,0
http://aclweb.org/anthology/D18-1019,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1019/d18-1019,0,1,GENIA,0,1,0,,,,0
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1068/d18-1068,0,1,Hardt and Rambow Dataset,0,0,0,,re-curated; original authors on paper,0,1
ExtRA: Extracting Prominent Review Aspects from Customer Feedback,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1384/d18-1384,0,0,Review Dataset,0,1,0,,newly curated,0,1
Interpretable Emoji Prediction via Label-Wise Attention LSTMs,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1508/d18-1508,0,1,SemEval 2018 Emoji Prediction,0,1,0,,,0,1
Interpretable Emoji Prediction via Label-Wise Attention LSTMs,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1508/d18-1509,0,1,Extended twitter corpus,0,1,0,,Newly created,,0
Reducing Gender Bias in Abusive Language Detection,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1302/d18-1302,0,0,Sexist Tweets,0,1,0,,,0,1
Reducing Gender Bias in Abusive Language Detection,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1302/d18-1303,0,0,Abusive Tweets,0,1,0,,,,0
Lexicosyntactic Inference in Neural Models,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1501/d18-1501,0,0,MegaVeridicality1,0,1,0,,,1,1
Lexicosyntactic Inference in Neural Models,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1501/d18-1502,0,0,MegaAttitude,0,1,0,,,,0
Lexicosyntactic Inference in Neural Models,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1501/d18-1503,0,0,MegaVeridicality2,0,1,0,,,,0
Syntactical Analysis of the Weaknesses of Sentiment Analyzers,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1141/d18-1141,0,0,Google Cloud Natural Language,0,1,0,,,0,1
Syntactical Analysis of the Weaknesses of Sentiment Analyzers,NLP,EMNLP,2018,https://aclanthology.info/papers/D18-1141/d18-1142,0,0,Stanford CoreNLP,0,1,0,,,,0
Learning Unsupervised Word Translations Without Adversaries,NLP,ACL,2018,http://aclweb.org/anthology/D18-1063,0,0,En->It,0,1,0,,,0,1
Learning Unsupervised Word Translations Without Adversaries,NLP,ACL,2018,http://aclweb.org/anthology/D18-1064,0,0,En-Sp,0,1,0,,,,0
Learning Unsupervised Word Translations Without Adversaries,NLP,ACL,2018,http://aclweb.org/anthology/D18-1065,0,0,En-Ch,0,1,0,,,,0
Learning Unsupervised Word Translations Without Adversaries,NLP,ACL,2018,http://aclweb.org/anthology/D18-1066,0,0,MUSE,0,1,0,,,,0
"Early Recognition of Sepsis with Gaussian Process Temporal
Convolutional Networks and Dynamic Time Warping",ML4H,MLHC,2019,https://www.mlforhc.org/s/Moor.pdf,0,1,MIMIC-III,1,1,1,,,1,
"Relaxed Parameter Sharing: Effectively Modeling
Time-Varying Relationships in Clinical Time-Series",ML4H,MLHC,2019,https://www.mlforhc.org/s/Oh.pdf,0,1,Synthetic Dataset,0,0,0,,,1,
"Relaxed Parameter Sharing: Effectively Modeling
Time-Varying Relationships in Clinical Time-Series",ML4H,MLHC,2019,https://www.mlforhc.org/s/Oh.pdf,0,1,MIMIC-III,1,1,1,,,1,
FLARe: Forecasting by Learning Anticipated Representations,ML4H,MLHC,2019,https://www.mlforhc.org/s/Devarakonda.pdf,0,1,ADNI,1,1,1,,,0,
"Multi-Task Gaussian Processes and Dilated Convolutional
Networks for Reconstruction of Reproductive Hormonal
Dynamics",ML4H,MLHC,2019,https://www.mlforhc.org/s/Urteaga.pdf,0,1,Synthetic Dataset,0,0,0,,,0,
Using Contextual Information to Improve Blood Glucose Prediction,ML4H,MLHC,2019,https://www.mlforhc.org/s/Akbari.pdf,0,0,CGM data,1,1,1,,,1,
Using Contextual Information to Improve Blood Glucose Prediction,ML4H,MLHC,2019,https://www.mlforhc.org/s/Akbari.pdf,0,0,social media data,1,1,1,,,1,
Dynamically Personalized Detection of Hemorrhage,ML4H,MLHC,2019,https://www.mlforhc.org/s/Nagpal.pdf,0,0,lab animal dataset,0,0,0,,,1,
Multiple Instance Learning for ECG Risk Stratification,ML4H,MLHC,2019,https://www.mlforhc.org/s/Shanmugam.pdf,0,0,MERLIN-TIMI,1,0,0,,,1,
"A Spatiotemporal Approach to Predicting Glaucoma
Progression Using a CT-HMM",ML4H,MLHC,2019,https://www.mlforhc.org/s/Nagesh.pdf,0,0,self collected,1,0,0,,,1,
"Temporal Graph Convolutional Networks for Automatic
Seizure Detection",ML4H,MLHC,2019,https://www.mlforhc.org/s/Cover.pdf,0,0,self collected,1,0,0,,,0,
"Meta-Weighted Gaussian Process Experts
for Personalized Forecasting of AD Cognitive Changes",ML4H,MLHC,2019,https://www.mlforhc.org/s/Rudovic.pdf,0,0,ADNI,1,1,1,,,1,
Multimodal Machine Learning for Automated ICD Coding,ML4H,MLHC,2019,https://www.mlforhc.org/s/Xu_K.pdf,0,0,MIMIC-III,1,1,1,,,0,
"Clinical Judgement Study using Question Answering from
Electronic Health Records",ML4H,MLHC,2019,https://www.mlforhc.org/s/Rawat.pdf,0,0,self collected,1,0,0,,,0,
"Self-Attention Based Molecule Representation for Predicting
Drug-Target Interaction",ML4H,MLHC,2019,https://www.mlforhc.org/s/Shin.pdf,0,0,PubChem database,0,1,0,,,1,
"Self-Attention Based Molecule Representation for Predicting
Drug-Target Interaction",ML4H,MLHC,2019,https://www.mlforhc.org/s/Shin.pdf,0,0,DrugBank,0,1,0,,,1,
Clinically Accurate Chest X-Ray Report Generation,ML4H,MLHC,2019,https://www.mlforhc.org/s/Liu_G.pdf,0,0,MIMIC-CXR,1,1,1,,,0,
Clinically Accurate Chest X-Ray Report Generation,ML4H,MLHC,2019,https://www.mlforhc.org/s/Liu_G.pdf,0,0,Open-I,1,1,1,,,0,
A Neural Model for Predicting Dementia from Language,ML4H,MLHC,2019,https://www.mlforhc.org/s/Kong.pdf,0,0,DementiaBank,1,1,1,,,1,
A Neural Model for Predicting Dementia from Language,ML4H,MLHC,2019,https://www.mlforhc.org/s/Kong.pdf,0,0,Dementia Blog Corpus,1,1,1,,,1,
Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis,ML4H,MLHC,2019,https://www.mlforhc.org/s/Guan.pdf,0,1,self collected,1,0,0,,,1,
"Predicting Phase 3 Clinical Trial Results by Modeling Phase
2 Clinical Trial Subject Level Data Using Deep Learning",ML4H,MLHC,2019,https://www.mlforhc.org/s/MLHC_YouranQi_QiTang_CameraReadyPaper-Sep-16-2019.pdf,0,0,self collected,1,0,0,,,0,
"Phenotype Inference with Semi-Supervised Mixed
Membership Models",ML4H,MLHC,2019,https://www.mlforhc.org/s/Rodriguez.pdf,0,0,MIMIC-III,1,1,1,,,0,
Counterfactual Reasoning for Fair Clinical Risk Prediction,ML4H,MLHC,2019,https://www.mlforhc.org/s/Pfohl.pdf,0,0,Stanford Medicine Research Data Repository,1,0,0,,,0,
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use,ML4H,MLHC,2019,https://www.mlforhc.org/s/Tonekaboni.pdf,,,,,,,,A survey to clinicians; not a techincal paper,,
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks,ML4H,MLHC,2019,https://www.mlforhc.org/s/Nestor.pdf,0,1,MIMIC-III,1,1,1,,,1,
Are Online Reviews of Physicians Biased Against Female Providers?,ML4H,MLHC,2019,https://www.mlforhc.org/s/Thawani.pdf,0,1,RateMDs.com corpus,0,1,0,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Atherosclerosis Risk in Communities Study (ARIC, 1987-2011);",1,1,1,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Cardiovascular Health Study (CHS, 1989-1999);",1,1,1,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Coronary Artery Risk Development in Young Adults (CARDIA, 1983-2006)",1,1,1,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Framingham Heart Study O↵- spring (FHS, 1971-2014);",1,1,1,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Jackson Heart Study (JHS, 2000-2012)",1,1,1,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Multi-Ethnic Study of Atherosclerosis (MESA, 2000-2012)",1,1,1,,,1,
ASAC: Active Sensing using Actor-Critic models,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yoon.pdf,0,0,ADNI,1,1,1,,,0,
ASAC: Active Sensing using Actor-Critic models,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yoon.pdf,0,0,MIMIC-II,1,1,1,,,0,
Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series,ML4H,MLHC,2019,https://www.mlforhc.org/s/Zheng.pdf,0,1,simulation,0,0,0,,,0,
Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series,ML4H,MLHC,2019,https://www.mlforhc.org/s/Zheng.pdf,0,1,the Diabetes Management Integrated Technology Research Initiative (DMITRI) dataset,1,1,1,,,0,
"The Medical Deconfounder: Assessing Treatment Eects with
Electronic Health Records (EHRs)",ML4H,MLHC,2019,https://www.mlforhc.org/s/Zhang_L.pdf,0,0,simulation,0,0,0,,,1,
"The Medical Deconfounder: Assessing Treatment Eects with
Electronic Health Records (EHRs)",ML4H,MLHC,2019,https://www.mlforhc.org/s/Zhang_L.pdf,0,0,Columbia University Medical Center database,1,0,0,,,1,
"EEGtoText: Learning to Write Medical Reports from EEG
Recordings",ML4H,MLHC,2019,https://www.mlforhc.org/s/Biswal.pdf,0,0,EEG Report Data set,1,0,0,,,0,
"EEGtoText: Learning to Write Medical Reports from EEG
Recordings",ML4H,MLHC,2019,https://www.mlforhc.org/s/Biswal.pdf,0,0,TUH EEG Report Data set,1,0,0,,,0,
Few-Shot Learning for Dermatological Disease Diagnosis,ML4H,MLHC,2019,https://www.mlforhc.org/s/Prabhu.pdf,0,0,Dermnet Skin Disease Atlas,0,1,0,,,1,
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images,ML4H,MLHC,2019,https://www.mlforhc.org/s/Dov.pdf,0,0,self collected,0,0,0,,,0,
Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis,ML4H,MLHC,2019,https://www.mlforhc.org/s/Kyono.pdf,0,0,tommy dataset,1,1,1,,,1,
Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis,ML4H,MLHC,2019,https://www.mlforhc.org/s/Kyono.pdf,0,0,CBIS-DDSM,1,1,1,,,1,
Enhancing high-content imaging for studying microtubule networks at large-scale,ML4H,MLHC,2019,https://www.mlforhc.org/s/Lee_HC.pdf,0,1,The Human Protein Atlas,0,1,0,,,1,
"Simultaneous Measurement Imputation and Outcome
Prediction for Achilles Tendon Rupture Rehabilitation",ML4H,MLHC,2019,https://www.mlforhc.org/s/Hamesse.pdf,0,0,Achilles Tendon Rupture (ATR) cohort,1,0,0,,,1,
Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments,ML4H,MLHC,2019,https://www.mlforhc.org/s/Mirtchouk.pdf,0,1,"multimodality ACE lab (Merck et al., 2016)",1,1,1,http://www.healthailab.org/data.html.,,0,
Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments,ML4H,MLHC,2019,https://www.mlforhc.org/s/Mirtchouk.pdf,0,1,"free-living (Mirtchouk et al., 2017) datasets",1,1,1,http://www.healthailab.org/data.html.,,0,
"Embryo Staging with Weakly-Supervised Region Selection
and Dynamically-Decoded Predictions",ML4H,MLHC,2019,https://www.mlforhc.org/s/Lau.pdf,0,1,self collected,0,0,0,,,0,
Measuring the Sympathetic Response to Intense Exercise in a Practical Setting,ML4H,MLHC,2019,https://www.mlforhc.org/s/Kaul.pdf,0,0,self collected,1,0,0,,,0,
Learning from Few Subjects with Large Amounts of Voice Monitoring Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Gonzalez_Ortiz.pdf,0,0,self collected,1,0,0,,,1,
"SLEEPER: interpretable Sleep staging via Prototypes from
Expert Rules",ML4H,MLHC,2019,https://www.mlforhc.org/s/Al-Hussaini.pdf,0,0,MGH,1,0,0,,,0,
"SLEEPER: interpretable Sleep staging via Prototypes from
Expert Rules",ML4H,MLHC,2019,https://www.mlforhc.org/s/Al-Hussaini.pdf,0,0,ISRUC,1,1,1,,,0,
Medical Concept Normalization by Encoding Target Knowledge,ML4H,ML4H,2019,Not-yet-public (ML4H '19 311),0,1,CSIRO Adverse Drug Event Corpus (CADEC),0,1,0,"Sarvnaz Karimi, Alejandro Metke-Jimenez, Madonna Kemp, and Chen Wang. Cadec: A corpus of adverse drug event annotations. Journal of biomedical informatics, 55:73–81, 2015. https://yadi.sk/d/GZoWm1wBxzyW_w",,0,
Medical Concept Normalization by Encoding Target Knowledge,ML4H,ML4H,2019,Not-yet-public (ML4H '19 311),0,1,Psychiatric Treatment Adverse Reactions (PsyTAR),0,1,0,"Abeed Sarker, Maksim Belousov, Jasper Friedrichs, Kai Hakala, Svetlana Kiritchenko, Farrokh Mehryary, Sifei Han, Tung Tran, Anthony Rios, Ramakanth Kavuluru, et al. Data and systems for medication-related text classification and concept normalization from twitter: insights from the social media mining for health (smm4h)-2017 shared task. 
 ournal of the American Medical Informatics Association, 25(10):1274–1283, 2018. https://doi.org/10.5281/zenodo.3236318",,0,
Medical Concept Normalization by Encoding Target Knowledge,ML4H,ML4H,2019,Not-yet-public (ML4H '19 311),0,1,SNOMED-CT,0,1,0,,,0,
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology,ML4H,ML4H,2019,Not-yet-public (ML4H '19 226),0,0,Self-collected teledermatology dataset,1,0,0,https://arxiv.org/pdf/1909.05382.pdf,,1,
Localization with Limited Annotation for Chest X-rays,ML4H,ML4H,2019,Not-yet-public (ML4H '19 73),0,0,Chest X-ray 14,1,1,1,https://nihcc.app.box.com/v/ChestXray-NIHCC,,0,
Privacy Preserving Human Fall Detection using Video Data,ML4H,ML4H,2019,Not-yet-public (ML4H '19 66),0,0,Multicamera Human fall detection dataset,1,1,1,http://www.iro.umontreal.ca/~labimage/Dataset/,,1,
Privacy Preserving Human Fall Detection using Video Data,ML4H,ML4H,2019,Not-yet-public (ML4H '19 66),0,0,Le2i Human fall detection database,1,1,1,http://le2i.cnrs.fr/Fall-detection-Dataset?lang=fr,,1,
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 300),0,1,MIMIC,1,1,1,,,1,
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 300),0,1,CASI,1,1,,https://www.ncbi.nlm.nih.gov/pubmed/23813539,,1,
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 300),0,1,i2b2,1,1,,,,1,
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 300),0,1,UMLS,0,1,,,,1,
Non-Invasive Silent Speech Recognition in Multiple Sclerosis with Dysphonia,ML4H,ML4H,2019,Not-yet-public (ML4H '19 49),0,0,self collected,1,0,,,,1,
Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements,ML4H,ML4H,2019,Not-yet-public (ML4H '19 223),0,1,UNBC McMaster Shoulder Pain Dataset,1,1,,http://www.consortium.ri.cmu.edu/painagree/,,1,
Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements,ML4H,ML4H,2019,Not-yet-public (ML4H '19 223),0,1,Child Postoperative Pain Dataset,1,0,,,,1,
On the design of convolutional neural networks for automatic detection of Alzheimer’s disease,ML4H,ML4H,2019,Not-yet-public (ML4H '19 233),0,1,ADNI,1,1,,,,1,
Predicting utilization of healthcare services from individual disease trajectories using RNNs with multi-headed attention,ML4H,ML4H,2019,Not-yet-public (ML4H '19 124),0,1,Register of Primary Health Care Visits (AvoHilmo) THO Finland,1,0,,,,1,
Generative Image Translation for Data Augmentation in Colorectal Histopathology Images,ML4H,ML4H,2019,Not-yet-public (ML4H '19 42),0,1,self collected,1,0,,,,1,
Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data,ML4H,ML4H,2019,Not-yet-public (ML4H '19 125),0,0,Sleep Hearth Health Study 1 (SHHS1),1,1,,https://sleepdata.org/datasets/shhs,,1,
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 247),0,1,AphasiaBank,1,1,1,https://aphasia.talkbank.org/,,1,
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 247),0,1,TED Talks datset,0,1,0,http://opus.nlpl.eu/TedTalks.php,,1,
Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images,ML4H,ML4H,2019,Not-yet-public (ML4H '19 286),0,0,self collected,1,0,0,,,1,
Generative Smoke Removal,ML4H,ML4H,2019,Not-yet-public (ML4H '19 94),0,1,Hamlyn Centre Laparoscopic / Endoscopic Video Datasets,1,1,1,hamlyn.doc.ic.ac.uk/vision,,0,
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection,ML4H,ML4H,2019,Not-yet-public (ML4H '19 232),0,1,LIDC,1,1,1,https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI,,0,
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection,ML4H,ML4H,2019,Not-yet-public (ML4H '19 232),0,1,self collected,1,0,0,,,0,
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection,ML4H,ML4H,2019,Not-yet-public (ML4H '19 232),0,1,simulated data,0,0,0,,,0,
Baselines for Chest X-Ray Report Generation,ML4H,ML4H,2019,Not-yet-public (ML4H '19 175),0,1,MIMIC-CXR,1,1,1,https://archive.physionet.org/physiobank/database/mimiccxr,,0,
Minimax Classifier with Box Constraint on the Priors,ML4H,ML4H,2019,Not-yet-public (ML4H '19 85),0,1,Framingham,1,1,1,https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset,,1,
Are Online Reviews of Physicians Biased Against Female Providers?,ML4H,MLHC,2019,https://www.mlforhc.org/s/Thawani.pdf,0,1,RateMDs.com corpus,0,1,0,https://thebiogrid.org/,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Atherosclerosis Risk in Communities Study (ARIC, 1987-2011);",1,1,1,,,0,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Cardiovascular Health Study (CHS, 1989-1999);",1,1,1,,A workshop summary paper,,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Coronary Artery Risk Development in Young Adults (CARDIA, 1983-2006)",1,1,1,,,1,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Framingham Heart Study O↵- spring (FHS, 1971-2014);",1,1,1,,,0,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Jackson Heart Study (JHS, 2000-2012)",1,1,1,,,0,
A Calibration Metric for Risk Scores with Survival Data,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yadlowsky.pdf,0,0,"Multi-Ethnic Study of Atherosclerosis (MESA, 2000-2012)",1,1,1,,,0,
ASAC: Active Sensing using Actor-Critic models,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yoon.pdf,0,0,ADNI,1,1,1,,,0,
ASAC: Active Sensing using Actor-Critic models,ML4H,MLHC,2019,https://www.mlforhc.org/s/Yoon.pdf,0,0,MIMIC-II,1,1,1,,,1,
Revisiting Parameter Estimation in Biological Networks: Influence of Symmetries,ML4H,HD-KDD2019,2019,https://www.biorxiv.org/content/10.1101/674739v1,0,1, BioGRID,0,1,0,,,1,
"Cell Images  Classification using Deep Convolutional Auto
encoder",ML4H,HD-KDD2019,2019,http://home.biokdd.org/biokdd19/camera_ready/BIOKDD%202019_camera_ready_cell_image_classification.pdf,0,,SNPHEp-2,0,1,0,,,1,
"Proceedings of the 
ACM SIGKDD Workshop on 
Epidemiology meets Data Mining and 
Knowledge Discovery (epiDAMIK) ",ML4H,HD-KDD2019,2019,http://people.cs.vt.edu/~badityap/epidamik/kdd-epidamik19-proceedings.pdf,n/a,n/a,,,,,,,1,
"Predicting assisted ventilation in Amyotrophic Lateral Sclerosis
using a mixture of experts and conformal predictors",ML4H,HD-KDD2019,2019,https://www.researchgate.net/publication/334561366_Predicting_assisted_ventilation_in_Amyotrophic_Lateral_Sclerosis_using_a_mixture_of_experts_and_conformal_predictors,0,0,private,,0,,https://math-qa.github.io/math-QA/,,0,
Understanding Behavior of Clinical Models under Domain Shifts,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1809.07806.pdf,0,0, MIMIC-III-EHR ,1,1,1,,,1,
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1034.pdf,0,0,CMU-MOSEI,0,1,0,,,1,
Tweet Stance Detection Using an Attention based Neural Ensemble Mode,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1185.pdf,0,0,SemEval-2016  Task-A ,0,1,0,,,1,
"Improving Robustness of Machine Translation with Synthetic Noise
",NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1190.pdf,0,0,Mtnt (2018),0,1,0,,,1,
"Linguistically-Informed Specificity and Semantic Plausibility
for Dialogue Generation",NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1349.pdf,0,1,PersonaChat dataset,0,1,0,,,1,
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1116/,0,1,Wall Street Journal,0,1,0,,,0,
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1116/,0,1,MultiNLI,0,1,0,,,0,
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1116/,0,1,Penn Treebank,0,1,0,,,0,
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1245/,0,0,Math QA,0,1,0,,,0,
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1106/,0,0,Bibtex,0,1,0,https://physionet.org/physiobank/database/mitdb/,,1,
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1106/,0,0,Mediamill,0,1,0,,1. Human Activity Recognition Using Smartphones(HAR) 2.CASAS smart homes (HH101) dataset,0,
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1106/,0,0,Eurlex,0,1,0,,,0,
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1106/,0,0,Delicious,0,1,0,,Nestle SHIELD (NSH) selfie images,0,
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1106/,0,0,Wiki10-31K,0,1,0,,,0,
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1387/,0,0,IITB English-Hindi Parallel Corpus,0,1,0,https://github.com/,Lytro Illum LF Camera Datasets - Octopus,1,
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1387/,0,0,ILCI English-Hindi Parallel Corpus,0,1,0,https://github.com/,Lytro Illum LF Camera Datasets - House,1,
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation,NLP,NAACL-2019,2019,https://www.aclweb.org/anthology/N19-1411/,0,1,SNLI,0,1,0,https://github.com/,Lytro Illum LF Camera Datasets - Toycar,1,
Time-to-Event Prediction for Correlated Clinical Events based on State Space Model,ML4H,HD-KDD2019,2019,https://github.com/yuanxue/yuanxue.github.io/blob/master/KDD2019_workshop.pdf,0,0,MIMIC-III,1,1,1,https://github.com/,Lytro Illum LF Camera Datasets - Chamelon,1,
SOAR: Shrinkage Optimized Active Learning with Joint Regularization for Electrocardiographic Signal Classification,ML4H,HD-KDD2019,2019,https://drive.google.com/file/d/15f28g4SMslNT0K3TXBavpIPKLNnLPL35/view,0,0,MIT-BIH arrhythmia database,0,1,0,https://arxiv.org/abs/1612.00837,,0,
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.05597.pdf,0,0,self-collected,0,1,0,https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Klare_Pushing_the_Frontiers_2015_CVPR_paper.pdf,,1,
Finding Discriminative subgroups of Brain Regions using Tensor Factorisation,ML4H,HD-KDD2019,2019,https://github.com/yejinjkim/yejinjkim.github.io/blob/master/papers/DSHealth%20(1).pdf,0,0,Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset,1,1,1,http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html,,1,
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.07901.pdf,0,1,self-collected,0,1,0,https://www.sciencedirect.com/science/article/pii/S0262885614001012,,1,
Detection of Covariate Interactions by Deep Neural Network Models,ML4H,HD-KDD2019,2019,https://dshealthkdd.github.io/dshealth-2019/assets/DSHealth_2019_paper_11.pdf,0,0,Simulated data,0,1,0,https://scholar.google.com/scholar_url?url=https://link.springer.com/article/10.1007/s11263-015-0816-y&hl=en&sa=T&oi=gsb-ggp&ct=res&cd=0&d=18051189604458238351&ei=O57EXf-0KoSSmAGLp4u4Bw&scisig=AAGBfm0c911-F-EiDOOkJW-7J15tnCh2fA,,1,
"Large-Scale, Metric Structure from Motion for Unordered Light Fields",CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Nousias_Large-Scale_Metric_Structure_From_Motion_for_Unordered_Light_Fields_CVPR_2019_paper.pdf,0,1,self-collected,0,1,0,https://scholar.google.com/scholar_url?url=https://link.springer.com/article/10.1007/s11263-009-0275-4&hl=en&sa=T&oi=gsb&ct=res&cd=0&d=2737532761291704147&ei=GJ7EXZeDOIeQmAG3pIS4CA&scisig=AAGBfm3EXPyiB49Uj3pGW2poLaDlHNZX1w,,1,
"Large-Scale, Metric Structure from Motion for Unordered Light Fields",CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Nousias_Large-Scale_Metric_Structure_From_Motion_for_Unordered_Light_Fields_CVPR_2019_paper.pdf,0,1,self-collected,0,1,0,https://scholar.google.com/scholar_url?url=https://link.springer.com/chapter/10.1007/978-3-319-24553-9_68&hl=en&sa=T&oi=gsb&ct=res&cd=0&d=16006066910313117644&ei=Vp7EXZyALcHRsQLEv6O4Bw&scisig=AAGBfm0QQoTkrO-V0eBoqDxe0rOOZ9ckyw,,1,
"Large-Scale, Metric Structure from Motion for Unordered Light Fields",CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Nousias_Large-Scale_Metric_Structure_From_Motion_for_Unordered_Light_Fields_CVPR_2019_paper.pdf,0,1,self-collected,0,1,0,http://medicaldecathlon.com/,pancreas tumor subset,1,
"Large-Scale, Metric Structure from Motion for Unordered Light Fields",CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Nousias_Large-Scale_Metric_Structure_From_Motion_for_Unordered_Light_Fields_CVPR_2019_paper.pdf,0,1,self-collected,0,1,0,https://i2b2.org/NLP/DataSets/,Access to data requires registration and subject to data use agreement,0,
Deep Modular Co-Attention Networks for Visual Question Answering,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Deep_Modular_Co-Attention_Networks_for_Visual_Question_Answering_CVPR_2019_paper.pdf,0,0,VQA-v2,0,1,0,,,0,
Unsupervised Face Normalization with Extreme Pose and Expression in the Wild,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Qian_Unsupervised_Face_Normalization_With_Extreme_Pose_and_Expression_in_the_CVPR_2019_paper.pdf,0,1,IJB-A,0,1,0,https://www.ifcc.org/ifcc-scientific-division/sd-committees/c-npu/,,1,
Unsupervised Face Normalization with Extreme Pose and Expression in the Wild,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Qian_Unsupervised_Face_Normalization_With_Extreme_Pose_and_Expression_in_the_CVPR_2019_paper.pdf,0,1,CMU Multi-PIE,0,1,0,,,1,
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Joint_Representation_and_Estimator_Learning_for_Facial_Action_Unit_Intensity_CVPR_2019_paper.pdf,0,0,BP4D-spontaneous,0,1,0,,Yahoo ads log data,1,
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wei_Iterative_Reorganization_With_Weak_Spatial_Constraints_Solving_Arbitrary_Jigsaw_Puzzles_CVPR_2019_paper.pdf,0,0,ILSVRC2012,0,1,0,https://viznet.media.mit.edu/,https://sherlock.media.mit.edu/,1,
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wei_Iterative_Reorganization_With_Weak_Spatial_Constraints_Solving_Arbitrary_Jigsaw_Puzzles_CVPR_2019_paper.pdf,0,0,Pascal VOC 2007,0,1,0,http://www.image-net.org/,,1,
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wei_Iterative_Reorganization_With_Weak_Spatial_Constraints_Solving_Arbitrary_Jigsaw_Puzzles_CVPR_2019_paper.pdf,0,0,NIH pancreas segmentation,1,1,1,http://faust.is.tue.mpg.de/,,1,
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wei_Iterative_Reorganization_With_Weak_Spatial_Constraints_Solving_Arbitrary_Jigsaw_Puzzles_CVPR_2019_paper.pdf,0,0,Medical Segmentation Decathlon,1,1,1,http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html,,1,
Cost-aware Active Learning for Named Entity Recognition in Clinical Text,ML4H,HD-KDD2019,2019,https://sbmi.uth.edu/ccb/publications/kdd-2019-workshop-final-rev.pdf,0,0,"i2b2/VA 2010 challenge data
",1,1,1,https://sites.google.com/site/ligb86/hkuis,,0,
Medical Concept Representation Learning from claims Data and Application to Health Plan Payment Risk Adjustment,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.06600.pdf,0,0,Regional US health insurance plan claim data,1,0,0,http://academictorrents.com/details/6c49defd6f0e417c039637475cde638d1363037e,,0,
Aggregate-Eliminate-Predict: Detecting Adverse Drug Eventsfrom Heterogeneous Electronic Health Records,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.06058.pdf,0,0,Stockholm EPR Corpus,1,0,0,http://saliencydetection.net/duts/,,0,
"Accurate Prediction of Medical Events based on Large
Healthcare Claim Data",ML4H,HD-KDD2019,2019,https://drive.google.com/file/d/1ikAuYYaBnVQeSr67xTZaexnnxn3PD_7H/view,0,0,Medicare Inpatient LDS SAF 2017,0,0,0,http://saliencydetection.net/dut-omron/,,0,
Carousel Ads Optimization in Yahoo Gemini Native,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/carousel-ads-optimization-in-yahoo-gemini-native,0,0,self-collected,0,0,0,,,0,
Sherlock: A Deep Learning Approach to Semantic Data Type Detection,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/sherlock-a-deep-learning-approach-to-semantic-data-type-detection,0,1,Viz-NEt,0,1,0,https://github.com/sriniiyer/concode,,0,
Chainer: a Deep Learning Framework for Accelerating the Research Cycle,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/chainer-a-deep-learning-framework-for-accelerating-the-research-cycle,0,1,ImageNet,0,1,0,https://amritasaha1812.github.io/CSQA/,,0,
A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes with Incompatible Shape Structures,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_A_Robust_Local_Spectral_Descriptor_for_Matching_Non-Rigid_Shapes_With_CVPR_2019_paper.pdf,0,0,Extension of FAUST,0,1,0,,,0,
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Cascaded_Partial_Decoder_for_Fast_and_Accurate_Salient_Object_Detection_CVPR_2019_paper.pdf,0,1,ECSSD,0,1,0,,,0,
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Cascaded_Partial_Decoder_for_Fast_and_Accurate_Salient_Object_Detection_CVPR_2019_paper.pdf,0,1,HKU-IS,0,1,0,,,0,
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Cascaded_Partial_Decoder_for_Fast_and_Accurate_Salient_Object_Detection_CVPR_2019_paper.pdf,0,1,PASCAL-S,0,1,0,,,0,
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Cascaded_Partial_Decoder_for_Fast_and_Accurate_Salient_Object_Detection_CVPR_2019_paper.pdf,0,1,DUTS,0,1,0,,,0,
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection,CV,CVPR-2019,2019,http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Cascaded_Partial_Decoder_for_Fast_and_Accurate_Salient_Object_Detection_CVPR_2019_paper.pdf,0,1,DUT-OMRON,0,1,0,,,0,
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1111/,0,0,Ramayana,0,0,0,,RCT,0,
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1082/,0,0,CONCODE,0,1,0,,,0,
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1082/,0,0,CSQA,0,1,0,,,0,
A Systematic Approach to Detect Hierarchical Healthcare Cost Drivers and Interpretable Change Patterns,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.08237,0,0,IBM Watson Health MarketScan Commercial Database,1,0,0,,,0,
A Systematic Approach to Detect Hierarchical Healthcare Cost Drivers and Interpretable Change Patterns,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.08237,0,0,RED BOOK databas,0,0,0,,,0,
Computational Phenotype Discovery via Probabilistic Independence,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.11051,0,0,Vanderbilt’s Electronic Health Record,1,0,0,,,0,
Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.03167,0,0,Twitter data,0,1,0,,,1,
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.08400,0,0,"UniProt [15],
Pfam [4], BRENDA [8], CAZy [3], PubChem [10], ChEMBL [6], KEGG [9, 11], The NCBI Taxonomy
database [5], GenBank [1] and PDB [2]",0,1,0,,,0,
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.08400,0,0,Handbook of Carbohydrate Engineering [16] and scientific articles,0,1,0,,Alibaba,0,
An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.07713.pdf,0,0,CAIRO,0,0,0,,Alibaba,0,
Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approach,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.06162.pdf,0,0,MIMIC-III,1,1,1,,,1,
Evaluation of Embeddings of Laboratory Test Codes for Patients at a Cancer Center,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.09600,0,1,self-collected,1,0,0,,,0,
Infant Mortality Prediction using Birth Certificate Data,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.08968,0,0,self-collected,1,0,0,,,0,
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.08650,0,1,UMLS semantic network,0,1,0,,,0,
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences,ML4H,HD-KDD2019,2019,https://arxiv.org/pdf/1907.07366.pdf,0,0,self-collected,1,0,0,,,0,
SLAM Endoscopy enhanced by adversarial depth prediction,ML4H,HD-KDD2019,2019,https://arxiv.org/abs/1907.00283,0,0,NIH Cancer Imaging Archive,0,1,0,,,0,
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/deep-spatio-temporal-neural-networks-for-click-through-rate-prediction,0,1,Avito advertising dataset,0,1,0,,,0,
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/deep-spatio-temporal-neural-networks-for-click-through-rate-prediction,0,1,Search advertising dataset,0,0,0,,,0,
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/deep-spatio-temporal-neural-networks-for-click-through-rate-prediction,0,1,News feed advertising dataset,0,0,0,,meteorological data,0,
Link Prediction with Signed Latent Factors in Signed Social Networks,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/defend-explainable-fake-news-detection,0,1,WikiElec,0,1,0,,,0,
Link Prediction with Signed Latent Factors in Signed Social Networks,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/defend-explainable-fake-news-detection,0,1,Slashdot,0,1,0,,,0,
dEFEND: Explainable Fake News Detection,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/link-prediction-with-signed-latent-factors-in-signed-social-networks,0,1,PolitiFact,0,1,0,,,0,
dEFEND: Explainable Fake News Detection,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/link-prediction-with-signed-latent-factors-in-signed-social-networks,0,1,GossipCop,0,1,0,,,0,
A Deep Value-network Based Approach for Multi-Driver Order Dispatching,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/a-deep-value-network-based-approach-for-multi-driver-order-dispatching,0,0,self-collected,0,0,0,,,1,
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/deepurbanevent-a-system-for-predicting-citywide-crowd-dynamics-at-big-event,0,1,self-collected,0,0,0,,,0,
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/co-prediction-of-multiple-transportation-demands-based-on-deep-spatio-tempo,0,0,NYC Citi Bike,0,1,0,,,0,
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/co-prediction-of-multiple-transportation-demands-based-on-deep-spatio-tempo,0,0,NYC Taxi,0,1,0,,,0,
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/co-prediction-of-multiple-transportation-demands-based-on-deep-spatio-tempo,0,0,External Factors,0,1,0,,,0,
Optimizing Peer Learning in Online Groups with Affinities,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/optimizing-peer-learning-in-online-groups-with-affinities,0,0,self-collected,0,0,0,,,1,
ProGAN: Network Embedding via Proximity Generative Adversarial Network,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/progan-network-embedding-via-proximity-generative-adversarial-network,0,0,Citeseer,0,1,0,,,0,
ProGAN: Network Embedding via Proximity Generative Adversarial Network,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/progan-network-embedding-via-proximity-generative-adversarial-network,0,0,Cora,0,1,0,,,0,
ProGAN: Network Embedding via Proximity Generative Adversarial Network,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/progan-network-embedding-via-proximity-generative-adversarial-network,0,0,Flickr,0,1,0,,,0,
Identifiability of Cause and Effect using Regularized Regression,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/identifiability-of-cause-and-effect-using-regularized-regression,0,1,five data sets from Mooij et al.,0,1,0,,,1,
Identifiability of Cause and Effect using Regularized Regression,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/identifiability-of-cause-and-effect-using-regularized-regression,0,1,Tübingen benchmark data set,0,1,0,,,1,
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/characterizing-and-detecting-malicious-accounts-inprivacy-centric-mobile-so,0,0,self-collected,1,0,0,,,0,
Adversarial Learning on Heterogeneous Information Networks,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/adversarial-learning-on-heterogeneous-information-networks,0,0,DBLP,0,1,0,,,1,
Adversarial Learning on Heterogeneous Information Networks,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/adversarial-learning-on-heterogeneous-information-networks,0,0,Yelp,0,1,0,,,1,
Adversarial Learning on Heterogeneous Information Networks,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/adversarial-learning-on-heterogeneous-information-networks,0,0,AMiner,0,1,0,,,1,
Adversarial Learning on Heterogeneous Information Networks,General,KDD-2019,2019,https://www.kdd.org/kdd2019/accepted-papers/view/adversarial-learning-on-heterogeneous-information-networks,0,0,Movielens,0,1,0,,,1,
First-order Adversarial Vulnerability of Neural Networks and Input Dimension,General,ICML-2019,2019,http://proceedings.mlr.press/v97/simon-gabriel19a.html,1,1,CIFAR-10,0,1,0,,,1,
On Symmetric Losses for Learning from Corrupted Labels,General,ICML-2019,2019,http://proceedings.mlr.press/v97/charoenphakdee19a.html,0,1,UCI,0,1,0,,,1,
On Symmetric Losses for Learning from Corrupted Labels,General,ICML-2019,2019,http://proceedings.mlr.press/v97/charoenphakdee19a.html,0,1,LIBSVM,0,1,0,,,1,
On Symmetric Losses for Learning from Corrupted Labels,General,ICML-2019,2019,http://proceedings.mlr.press/v97/charoenphakdee19a.html,0,1,MNIST,0,1,0,,,1,
On Symmetric Losses for Learning from Corrupted Labels,General,ICML-2019,2019,http://proceedings.mlr.press/v97/charoenphakdee19a.html,0,1,CIFAR-10,0,1,0,,,1,
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,General,ICML-2019,2019,http://proceedings.mlr.press/v97/chen19f.html,0,1,Movielens,0,1,0,,,1,
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,General,ICML-2019,2019,http://proceedings.mlr.press/v97/chen19f.html,0,1,Last.fm,0,1,0,,,1,
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,General,ICML-2019,2019,http://proceedings.mlr.press/v97/chen19f.html,0,1,Yelp,0,1,0,,,1,
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,General,ICML-2019,2019,http://proceedings.mlr.press/v97/chen19f.html,0,1,Taobao,0,0,0,,,1,
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,General,ICML-2019,2019,http://proceedings.mlr.press/v97/chen19f.html,0,1,RecSys15 YooChoose,0,1,0,,,1,
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,General,ICML-2019,2019,http://proceedings.mlr.press/v97/chen19f.html,0,1,Ant Financial News dataset,0,0,0,,,1,
Learning to Optimize Multigrid PDE Solvers,General,ICML-2019,2019,http://proceedings.mlr.press/v97/greenfeld19a.html,1,0,Simulated data,0,0,0,,,1,
On Discriminative Learning of Prediction Uncertainty,General,ICML-2019,2019,http://proceedings.mlr.press/v97/franc19a.html,0,1,UCI,0,1,0,,,1,
On Discriminative Learning of Prediction Uncertainty,General,ICML-2019,2019,http://proceedings.mlr.press/v97/franc19a.html,0,1,LIBSVM,0,1,0,,,1,
Conditional Independence in Testing Bayesian Networks,General,ICML-2019,2019,http://proceedings.mlr.press/v97/shen19a.html,1,0,Simulated data,0,0,0,,,0,
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization,General,ICML-2019,2019,http://proceedings.mlr.press/v97/ji19a.html,1,0,LIBSVM 1,0,1,0,,,1,
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization,General,ICML-2019,2019,http://proceedings.mlr.press/v97/ji19a.html,1,0,LIBSVM 2,0,1,0,,,1,
Composing Value Functions in Reinforcement Learning,General,ICML-2019,2019,http://proceedings.mlr.press/v97/van-niekerk19a.html,0,1,video game,0,1,0,,,1,
On the Impact of the Activation Function on Deep Neural Networks Training,General,ICML-2019,2019,http://proceedings.mlr.press/v97/hayou19a.html,1,1,CIFAR10,0,1,0,,,1,
On the Impact of the Activation Function on Deep Neural Networks Training,General,ICML-2019,2019,http://proceedings.mlr.press/v97/hayou19a.html,1,1,MNIST,0,1,0,,,1,
Efficient Full-Matrix Adaptive Regularization,General,ICML-2019,2019,http://proceedings.mlr.press/v97/agarwal19b.html,0,1,Simulated data,0,0,0,,,0,
Efficient Full-Matrix Adaptive Regularization,General,ICML-2019,2019,http://proceedings.mlr.press/v97/agarwal19b.html,0,1,CIFAR-10,0,1,0,,,0,
Calibrated Model-Based Deep Reinforcement Learning,General,ICML-2019,2019,http://proceedings.mlr.press/v97/malik19a.html,0,1,LinUCB,0,1,0,,,1,
Calibrated Model-Based Deep Reinforcement Learning,General,ICML-2019,2019,http://proceedings.mlr.press/v97/malik19a.html,0,1,Corporacion Favorita Kaggle,0,1,0,,,1,
Calibrated Model-Based Deep Reinforcement Learning,General,ICML-2019,2019,http://proceedings.mlr.press/v97/malik19a.html,0,1,OpenAI Gym,0,1,0,,introduced by the authors,1,
Towards a Deep and Unified Understanding of Deep Neural Models in NLP,General,ICML-2019,2019,http://proceedings.mlr.press/v97/guan19a.html,0,1,Simulated data,0,0,0,,,0,
Towards a Deep and Unified Understanding of Deep Neural Models in NLP,General,ICML-2019,2019,http://proceedings.mlr.press/v97/guan19a.html,0,1,SST-2,0,1,0,,,0,
Towards a Deep and Unified Understanding of Deep Neural Models in NLP,General,ICML-2019,2019,http://proceedings.mlr.press/v97/guan19a.html,0,1,CoLA,0,1,0,,,0,
Towards a Deep and Unified Understanding of Deep Neural Models in NLP,General,ICML-2019,2019,http://proceedings.mlr.press/v97/guan19a.html,0,1,QQP,0,1,0,,,0,
Remember and Forget for Experience Replay,General,ICML-2019,2019,http://proceedings.mlr.press/v97/novati19a.html,0,1,MuJoCo ,0,1,0,,,1,
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE,General,ICML-2019,2019,http://proceedings.mlr.press/v97/ma19c.html,0,1,MNIST,0,1,0,,,1,
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE,General,ICML-2019,2019,http://proceedings.mlr.press/v97/ma19c.html,0,1,UCI,0,1,0,,,1,
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE,General,ICML-2019,2019,http://proceedings.mlr.press/v97/ma19c.html,0,1,MIMIC-III,1,1,1,,,1,
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1598/,0,1,Linked WikiText-2,0,1,0,,,0,
"Enhancing Topic-to-Essay Generation with External Commonsense
Knowledge",NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1193/,0,0,ZHIHU corpus ,0,1,0,,,0,
AMR Parsing as Sequence-to-Graph Transduction,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1009/,0,1,AMR,0,1,0,,,1,
"Errudite: Scalable, Reproducible, and Testable Error Analysis",NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1073/,0,1,SQuAD,0,1,0,,,1,
Detecting Concealed Information in Text and Speech,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1039/,0,0,self-collected,0,0,0,,,0,
Semantic expressive capacity with bounded memory,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1008/,1,0,,,,,,,0,
Predicting Human Activities from User-Generated Content,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1245/,0,0,Twitter,0,1,0,,,0,
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1162/,0,0,"a labeled positive/negative
sentiment training set",0,1,0,,,0,
A Multi-Task Architecture on Relevance-based Neural Query Translation,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1639/,0,0,"Cross-Language
Evaluation Forum (CLEF)",0,1,0,,,1,
Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1172/,0,0,Bible data,0,1,0,,,0,
What does BERT learn about the structure of language?,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1356/,0,1,SNLI corpus,0,1,0,,,0,
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1196/,0,1,Wiki10K,0,1,0,,,0,
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1196/,0,1,Wiki200K,0,1,0,,,0,
"Analyzing Linguistic Differences between
Owner and Staff Attributed Tweets",NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1274/,0,0,Twitter self created,0,1,0,,,1,
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1372/,0,0,Weibo,0,1,0,,,0,
Joint Slot Filling and Intent Detection via Capsule Neural Networks,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1519/,0,0,SNIPS-NLU,0,1,0,,,1,
Joint Slot Filling and Intent Detection via Capsule Neural Networks,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1519/,0,0,ATIS,0,1,0,,,1,
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1018/,0,1,MUSE dataset,0,1,0,,,0,
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1018/,0,1,VecMap dataset,0,1,0,,,0,
Reliability-aware Dynamic Feature Composition for Name Tagging,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1016/,0,1,OntoNotes 5.0,0,1,0,,,0,
Adaptive Attention Span in Transformers,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1032/,0,1,text8,0,1,0,,,0,
Adaptive Attention Span in Transformers,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1032/,0,1,enwik8,0,1,0,,,0,
Learning How to Active Learn by Dreaming,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1401/,0,1,Amazon product reviews,0,1,0,,,0,
Learning How to Active Learn by Dreaming,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1401/,0,1,gender profiling task in PAN 2017,0,1,0,,,0,
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1511/,0,1,ACE2005,0,1,0,,,0,
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1511/,0,1,GENIA,0,1,0,,,0,
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1511/,0,1,TACKBP2017,0,1,0,,,0,
Generating Question-Answer Hierarchies,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1224/,0,1,SQuAD,0,1,0,,,0,
Generating Question-Answer Hierarchies,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1224/,0,1,QuAC,0,1,0,,,0,
Generating Question-Answer Hierarchies,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1224/,0,1,CoQA,0,1,0,,,0,
You Write Like You Eat: Stylistic variation as a predictor of social stratification,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1246/,0,1,Yelp,0,1,0,,,0,
Multilingual Constituency Parsing with Self-Attention and Pre-Training,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1340/,0,1,SPMRL 2013/2014,0,1,0,,,0,
Multilingual Constituency Parsing with Self-Attention and Pre-Training,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1340/,0,1,WSJ,0,1,0,,,0,
Multilingual Constituency Parsing with Self-Attention and Pre-Training,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1340/,0,1,Chinese Treebank,0,1,0,,,0,
Mitigating Gender Bias in Natural Language Processing: Literature Review,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1159/,0,0,Winogender,0,1,0,,,0,
Mitigating Gender Bias in Natural Language Processing: Literature Review,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1159/,0,0,WinoBias,0,1,0,,,0,
Mitigating Gender Bias in Natural Language Processing: Literature Review,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1159/,0,0,GAP,0,1,0,,,0,
Mitigating Gender Bias in Natural Language Processing: Literature Review,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1159/,0,0,EEC,0,1,0,,,0,
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1609/,0,0,GoogComp,0,1,0,,,0,
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1609/,0,0,Newsela text simplification dataset,0,1,0,,,0,
Hubless Nearest Neighbor Search for Bilingual Lexicon Induction,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1399/,0,1,MUSE dataset,0,1,0,,,0,
Identifying Visible Actions in Lifestyle Vlogs,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1643/,0,1,self-collected,0,1,0,,,0,
Enhancing Air Quality Prediction with Social Media and Natural Language Processing,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1251/,0,0,Twitter,0,1,0,,,0,
Variance of average surprisal: a better predictor for quality of grammar from unsupervised PCFG induction,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1235/,0,0,"corpora
annotated with constituents",0,1,0,,,0,
Variance of average surprisal: a better predictor for quality of grammar from unsupervised PCFG induction,NLP,ACL-2019,2019,https://www.aclweb.org/anthology/P19-1235/,0,0,corpora annotated with dependencies,0,1,0,,,0,