{"action":"created","issue":{"url":"https://api.github.com/repos/tensorflow/probability/issues/51","repository_url":"https://api.github.com/repos/tensorflow/probability","labels_url":"https://api.github.com/repos/tensorflow/probability/issues/51/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/probability/issues/51/comments","events_url":"https://api.github.com/repos/tensorflow/probability/issues/51/events","html_url":"https://github.com/tensorflow/probability/issues/51","id":325794384,"number":51,"title":"Implicit reparameterization gradients","user":{"login":"dustinvtran","id":2569867,"avatar_url":"https://avatars1.githubusercontent.com/u/2569867?v=4","gravatar_id":"","url":"https://api.github.com/users/dustinvtran","html_url":"https://github.com/dustinvtran","followers_url":"https://api.github.com/users/dustinvtran/followers","following_url":"https://api.github.com/users/dustinvtran/following{/other_user}","gists_url":"https://api.github.com/users/dustinvtran/gists{/gist_id}","starred_url":"https://api.github.com/users/dustinvtran/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dustinvtran/subscriptions","organizations_url":"https://api.github.com/users/dustinvtran/orgs","repos_url":"https://api.github.com/users/dustinvtran/repos","events_url":"https://api.github.com/users/dustinvtran/events{/privacy}","received_events_url":"https://api.github.com/users/dustinvtran/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-05-23T17:01:08Z","updated_at":"2018-05-24T17:50:58Z","closed_at":null,"author_association":"MEMBER","body":"The idea is simple to implement and well-scoped as part of TF Distributions. I personally like the idea of even having it be the default for Gamma, Beta, and others. The gradient implementations may be tricky. Maybe @mfigurnov has an implementation we can build on? (if open source or google-internal)\r\n\r\n+ https://arxiv.org/abs/1805.08498\r\n+ https://twitter.com/mfigurnov/status/999198094905499648\r\n+ https://twitter.com/ericjang11/status/999183266237198336"},"comment":{"url":"https://api.github.com/repos/tensorflow/probability/issues/comments/391802906","html_url":"https://github.com/tensorflow/probability/issues/51#issuecomment-391802906","issue_url":"https://api.github.com/repos/tensorflow/probability/issues/51","id":391802906,"user":{"login":"davmre","id":991882,"avatar_url":"https://avatars2.githubusercontent.com/u/991882?v=4","gravatar_id":"","url":"https://api.github.com/users/davmre","html_url":"https://github.com/davmre","followers_url":"https://api.github.com/users/davmre/followers","following_url":"https://api.github.com/users/davmre/following{/other_user}","gists_url":"https://api.github.com/users/davmre/gists{/gist_id}","starred_url":"https://api.github.com/users/davmre/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/davmre/subscriptions","organizations_url":"https://api.github.com/users/davmre/orgs","repos_url":"https://api.github.com/users/davmre/repos","events_url":"https://api.github.com/users/davmre/events{/privacy}","received_events_url":"https://api.github.com/users/davmre/received_events","type":"User","site_admin":false},"created_at":"2018-05-24T17:50:58Z","updated_at":"2018-05-24T17:50:58Z","author_association":"COLLABORATOR","body":"I imagine that getting a full-fledged C++ AD system into TF would be quite a heavy lift, but since this approach isn't AD'ing user code, just a small number of well-understodd computations (e.g., the gamma CDF), couldn't one just do the AD manually? That is, insofar as forward-mode AD is 'just' running your computation on dual numbers `x+eps` instead of `x`, for any given algorithm you should be able to just explicitly do this, grounding out the real and infinitesimal parts in your code."}}
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