{"action":"closed","issue":{"url":"https://api.github.com/repos/christophM/interpretable-ml-book/issues/15","repository_url":"https://api.github.com/repos/christophM/interpretable-ml-book","labels_url":"https://api.github.com/repos/christophM/interpretable-ml-book/issues/15/labels{/name}","comments_url":"https://api.github.com/repos/christophM/interpretable-ml-book/issues/15/comments","events_url":"https://api.github.com/repos/christophM/interpretable-ml-book/issues/15/events","html_url":"https://github.com/christophM/interpretable-ml-book/issues/15","id":288663590,"node_id":"MDU6SXNzdWUyODg2NjM1OTA=","number":15,"title":"Write a chapter about your favorite deep learning interpretability method","user":{"login":"christophM","id":1632100,"node_id":"MDQ6VXNlcjE2MzIxMDA=","avatar_url":"https://avatars0.githubusercontent.com/u/1632100?v=4","gravatar_id":"","url":"https://api.github.com/users/christophM","html_url":"https://github.com/christophM","followers_url":"https://api.github.com/users/christophM/followers","following_url":"https://api.github.com/users/christophM/following{/other_user}","gists_url":"https://api.github.com/users/christophM/gists{/gist_id}","starred_url":"https://api.github.com/users/christophM/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/christophM/subscriptions","organizations_url":"https://api.github.com/users/christophM/orgs","repos_url":"https://api.github.com/users/christophM/repos","events_url":"https://api.github.com/users/christophM/events{/privacy}","received_events_url":"https://api.github.com/users/christophM/received_events","type":"User","site_admin":false},"labels":[{"id":566460626,"node_id":"MDU6TGFiZWw1NjY0NjA2MjY=","url":"https://api.github.com/repos/christophM/interpretable-ml-book/labels/help%20wanted","name":"help wanted","color":"128A0C","default":true}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2018-01-15T17:10:25Z","updated_at":"2019-01-02T08:54:50Z","closed_at":"2019-01-02T08:54:50Z","author_association":"OWNER","body":"There are many interpretability methods for deep neural networks. \r\nThe field is very young and most methods are  quite new and under development. It's hard for a single person to keep track. This issue serves as a placeholder for interpretability methods specific for deep learning. \r\n\r\nLeave a comment if you are interested in adding a chapter. Any method that helps interpreting neural networks is interesting. A good starting point is to take a paper or some code library and explain it in simpler words, possibly with an example. \r\n\r\nSome possible starting points: \r\n\r\nSoftware: \r\n- https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5\r\n- https://github.com/yosinski/deep-visualization-toolbox\r\n- https://www.oreilly.com/ideas/interpretability-via-attentional-and-memory-based-interfaces-using-tensorflow\r\n- https://github.com/taolei87/rcnn\r\n\r\nScientific Paper: \r\n- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://arxiv.org/pdf/1703.03717v1.pdf)\r\n- [Rationalizing neural predictions](https://arxiv.org/abs/1606.04155)\r\n- [Generating Visual Explanations](https://arxiv.org/abs/1603.08507)\r\n- [Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations](https://arxiv.org/abs/1702.07826)\r\n- [Methods for interpreting and understanding deep neural networks](https://www.sciencedirect.com/science/article/pii/S1051200417302385)\r\n\r\nLeave a comment if you are interested in writing a chapter!"}}
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