P
Philip Bachman
Researcher at Microsoft
Publications - 53
Citations - 7595
Philip Bachman is an academic researcher from Microsoft. The author has contributed to research in topics: Reinforcement learning & Artificial neural network. The author has an hindex of 24, co-authored 53 publications receiving 5308 citations. Previous affiliations of Philip Bachman include University of Texas at Dallas & McGill University.
Papers
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Proceedings Article
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm,Alex Fedorov,Samuel Lavoie-Marchildon,Karan Grewal,Philip Bachman,Adam Trischler,Yoshua Bengio +6 more
TL;DR: Deep InfoMax (DIM) as discussed by the authors maximizes mutual information between an input and the output of a deep neural network encoder by matching to a prior distribution adversarially.
Proceedings Article
Learning Representations by Maximizing Mutual Information Across Views
TL;DR: This work develops a model which learns image representations that significantly outperform prior methods on the tasks the authors consider, and extends this model to use mixture-based representations, where segmentation behaviour emerges as a natural side-effect.
Posted Content
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm,Alex Fedorov,Samuel Lavoie-Marchildon,Karan Grewal,Philip Bachman,Adam Trischler,Yoshua Bengio +6 more
TL;DR: It is shown that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation’s suitability for downstream tasks and is an important step towards flexible formulations of representation learning objectives for specific end-goals.
Proceedings ArticleDOI
NewsQA: A Machine Comprehension Dataset
Adam Trischler,Tong Wang,Xingdi Yuan,Justin Harris,Alessandro Sordoni,Philip Bachman,Kaheer Suleman +6 more
TL;DR: NewsQA as mentioned in this paper ) is a dataset of over 100,000 human-generated question-answer pairs from CNN news articles, with answers consisting of spans of text in the articles.
Proceedings Article
Deep Reinforcement Learning That Matters
TL;DR: Challenges posed by reproducibility, proper experimental techniques, and reporting procedures are investigated and guidelines to make future results in deep RL more reproducible are suggested.