{"action":"opened","issue":{"url":"https://api.github.com/repos/AI-ON/Few-Shot-Music-Generation/issues/22","repository_url":"https://api.github.com/repos/AI-ON/Few-Shot-Music-Generation","labels_url":"https://api.github.com/repos/AI-ON/Few-Shot-Music-Generation/issues/22/labels{/name}","comments_url":"https://api.github.com/repos/AI-ON/Few-Shot-Music-Generation/issues/22/comments","events_url":"https://api.github.com/repos/AI-ON/Few-Shot-Music-Generation/issues/22/events","html_url":"https://github.com/AI-ON/Few-Shot-Music-Generation/issues/22","id":336255624,"node_id":"MDU6SXNzdWUzMzYyNTU2MjQ=","number":22,"title":"End-to-end learning of an LSTM with dynamic evaluation","user":{"login":"larocheh","id":1150814,"node_id":"MDQ6VXNlcjExNTA4MTQ=","avatar_url":"https://avatars0.githubusercontent.com/u/1150814?v=4","gravatar_id":"","url":"https://api.github.com/users/larocheh","html_url":"https://github.com/larocheh","followers_url":"https://api.github.com/users/larocheh/followers","following_url":"https://api.github.com/users/larocheh/following{/other_user}","gists_url":"https://api.github.com/users/larocheh/gists{/gist_id}","starred_url":"https://api.github.com/users/larocheh/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/larocheh/subscriptions","organizations_url":"https://api.github.com/users/larocheh/orgs","repos_url":"https://api.github.com/users/larocheh/repos","events_url":"https://api.github.com/users/larocheh/events{/privacy}","received_events_url":"https://api.github.com/users/larocheh/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-27T14:30:48Z","updated_at":"2018-06-27T14:30:48Z","closed_at":null,"author_association":"NONE","body":"Dynamic evaluation (see e.g. https://arxiv.org/abs/1709.07432) is a modification of how an LSTM is evaluated. However, training normally remains the same.\r\n\r\nHere, I propose that we also train the LSTM on the training set of episodes (support/query pairs) by taking into account the fact that the LSTM is doing dynamic updates on the support and query examples.\r\n\r\nTo simplify, I suggest we don't backpropagate through the dynamic updates themselves (i.e. avoid second derivatives), since there has been plenty of evidence that these aren't absolutely necessary for effective meta-learning."}}
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