{"action":"created","issue":{"url":"https://api.github.com/repos/liamb315/CharacterGAN/issues/4","repository_url":"https://api.github.com/repos/liamb315/CharacterGAN","labels_url":"https://api.github.com/repos/liamb315/CharacterGAN/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/liamb315/CharacterGAN/issues/4/comments","events_url":"https://api.github.com/repos/liamb315/CharacterGAN/issues/4/events","html_url":"https://github.com/liamb315/CharacterGAN/issues/4","id":153048768,"number":4,"title":"Backproping Through Argmax","user":{"login":"LeavesBreathe","id":14891677,"avatar_url":"https://avatars.githubusercontent.com/u/14891677?v=3","gravatar_id":"","url":"https://api.github.com/users/LeavesBreathe","html_url":"https://github.com/LeavesBreathe","followers_url":"https://api.github.com/users/LeavesBreathe/followers","following_url":"https://api.github.com/users/LeavesBreathe/following{/other_user}","gists_url":"https://api.github.com/users/LeavesBreathe/gists{/gist_id}","starred_url":"https://api.github.com/users/LeavesBreathe/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LeavesBreathe/subscriptions","organizations_url":"https://api.github.com/users/LeavesBreathe/orgs","repos_url":"https://api.github.com/users/LeavesBreathe/repos","events_url":"https://api.github.com/users/LeavesBreathe/events{/privacy}","received_events_url":"https://api.github.com/users/LeavesBreathe/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2016-05-04T15:56:25Z","updated_at":"2016-09-06T14:47:46Z","closed_at":null,"body":"Hey Liamb, really appreciate the work you're doing here. \r\n\r\nFor a long time, I've wanted to apply Adversarial networks to NLP. The main problem is that in a seq2seq generator, you have to use the argmax to predict the next word.\r\n\r\nThe problem with this is that you can't backprop through the argmax function -- it is non-differentiable. I thought of feeding the discriminator a direct embedding and doing nearest neighbors to predict best char/word.\r\n\r\nI noticed from your commits that you have had some instability. If you want to discuss this further, I would be happy to chat with you on skype. My username is 'leavesbreathe'. This is an exciting idea you have. I'm convinced this is the way to go after studying this paper:\r\n\r\nhttp://arxiv.org/abs/1511.05101\r\n\r\nThanks!"},"comment":{"url":"https://api.github.com/repos/liamb315/CharacterGAN/issues/comments/244974280","html_url":"https://github.com/liamb315/CharacterGAN/issues/4#issuecomment-244974280","issue_url":"https://api.github.com/repos/liamb315/CharacterGAN/issues/4","id":244974280,"user":{"login":"liamb315","id":1130820,"avatar_url":"https://avatars.githubusercontent.com/u/1130820?v=3","gravatar_id":"","url":"https://api.github.com/users/liamb315","html_url":"https://github.com/liamb315","followers_url":"https://api.github.com/users/liamb315/followers","following_url":"https://api.github.com/users/liamb315/following{/other_user}","gists_url":"https://api.github.com/users/liamb315/gists{/gist_id}","starred_url":"https://api.github.com/users/liamb315/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/liamb315/subscriptions","organizations_url":"https://api.github.com/users/liamb315/orgs","repos_url":"https://api.github.com/users/liamb315/repos","events_url":"https://api.github.com/users/liamb315/events{/privacy}","received_events_url":"https://api.github.com/users/liamb315/received_events","type":"User","site_admin":false},"created_at":"2016-09-06T14:47:46Z","updated_at":"2016-09-06T14:47:46Z","body":"Reinforcement learning, or specifically, REINFORCE, may be a compelling route forward to deal with non-differentiable operations in the graph. I'll keep you posted as I develop experiments."}}
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