The Building Blocks of Interpretability

Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space. With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building confidence about how they will behave in the real-world, detecting model bias, an

[takmin]

Deep Learning可視化ツール https://gigazine.net/news/20180312-neural-network-interpretability/

[suzukiMY]

『既存の解釈可能なメソッドをリッチなユーザーインターフェースにまとめることでニューラルネットワークの画像認識処理を人間が理解しやすくするという方法へのアイデア』

[Nyoho]

きもくていいね。DNN, deep neural networks