Published August 8, 2024
| Version v1
Dataset
Open
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
Creators
-
1.
École Polytechnique Fédérale de Lausanne
Description
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Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
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Authors: Lucas Stoffl, Andy Bonnetto, Stéphane D'Ascoli & Alexander Mathis
Affiliation: Ecole Polytechnique de Lausanne (EPFL)
Date: 25/09/2024
Link to the BiorXiv article : https://doi.org/10.1101/2024.08.06.606796
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Provided data (hBehaveMAE checkpoints)
We provide a collection of pre-trained models that were reported in our paper, allowing you to reproduce our results for MABe22, hBABEL and Shot7M2 datasets.
Note that you can download Shot7M2 on HuggingFace and generate hBABEL by following the instructions on the github page.
- hBehaveMAE_hBABEL.pth : checkpoint for the hBehaveMAE pre-trained on the hBABEL dataset
- hBehaveMAE_Shot7M2.pth : checkpoint for the hBehaveMAE pre-trained on the Shot7M2 dataset
- hBehaveMAE_MABe22.pth: checkpoint for the hBehaveMAE pre-trained on the MABe22 dataset
References
If you find our code, weights or ideas useful, please cite:
| @article {Stoffl2024hBehaveMAE, author = {Stoffl, Lucas and Bonnetto, Andy and d{\textquoteright}Ascoli, St{\'e}phane and Mathis, Alexander}, title = {Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders}, elocation-id = {2024.08.06.606796}, year = {2024}, doi = {10.1101/2024.08.06.606796}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796}, eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796.full.pdf}, journal = {bioRxiv} } |
Files
README.md
Additional details
Additional titles
- Alternative title
- BehaveMAE & Shot7M2
Related works
- Is part of
- Preprint: 10.1101/2024.08.06.606796 (DOI)
Software
- Repository URL
- https://github.com/amathislab/BehaveMAE
- Programming language
- Python
- Development Status
- Active