J
Jean Pouget-Abadie
Researcher at Université de Montréal
Publications - 25
Citations - 43672
Jean Pouget-Abadie is an academic researcher from Université de Montréal. The author has contributed to research in topics: Graph (abstract data type) & Computer science. The author has an hindex of 10, co-authored 21 publications receiving 32708 citations. Previous affiliations of Jean Pouget-Abadie include Google & Harvard University.
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Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Posted Content
Generative Adversarial Networks
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: In this article, a generative adversarial network (GAN) is proposed to estimate generative models via an adversarial process, in which two models are simultaneously trained: a generator G and a discriminator D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
Generative adversarial networks
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A generative adversarial networks algorithm designed to solve the generative modeling problem and its applications in medicine, education and robotics are studied.
Proceedings ArticleDOI
Detecting Network Effects: Randomizing Over Randomized Experiments
Martin Saveski,Jean Pouget-Abadie,Guillaume Saint-Jacques,Weitao Duan,Souvik Ghosh,Ya Xu,Edoardo M. Airoldi +6 more
TL;DR: A new experimental design is leverage for testing whether SUTVA holds, without making any assumptions on how treatment effects may spill over between the treatment and the control group, and the proposed methodology can be applied to settings in which a network is not necessarily observed but, if available, can be used in the analysis.
Proceedings ArticleDOI
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation
Jean Pouget-Abadie,Dzmitry Bahdanau,Bart van Merriënboer,Kyunghyun Cho,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +6 more
TL;DR: A way to address the issue of a significant drop in translation quality when translating long sentences by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model.