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Learning with Graph Kernels in the Chemical Universe

Tang, Yu-Hang


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{
  "description": "<p>Presentations slides of <a href=\"https://crd.lbl.gov/departments/computational-science/ccmc/staff/alvarez-fellows/yu-hang-tang/\">Yu-Hang Tang</a>&nbsp;during the 2019 <a href=\"https://data-science.llnl.gov/latest/workshop-2019#Day%202\">DSI Data Science Workshop</a>&nbsp;on application of active machine learning and graph kernels. The talk also features the release of the <a href=\"https://pypi.org/project/graphdot/\">GraphDot</a> library.</p>", 
  "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Lawrence Berkeley National Laboratory", 
      "@id": "https://orcid.org/0000-0001-7424-5439", 
      "@type": "Person", 
      "name": "Tang, Yu-Hang"
    }
  ], 
  "url": "https://zenodo.org/record/3364078", 
  "datePublished": "2019-08-08", 
  "keywords": [
    "machine learning", 
    "graph", 
    "active learning", 
    "molecular prediction", 
    "computational chemistry", 
    "kernel", 
    "similarity"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3364078", 
  "@id": "https://doi.org/10.5281/zenodo.3364078", 
  "@type": "PresentationDigitalDocument", 
  "name": "Learning with Graph Kernels in the Chemical Universe"
}
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