Search Results for author: Jean Pouget-Abadie

Found 6 papers, 2 papers with code

Variance Reduction in Bipartite Experiments through Correlation Clustering

no code implementations NeurIPS 2019 Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

This paper introduces a novel clustering objective and a corresponding algorithm that partitions a bipartite graph so as to maximize the statistical power of a bipartite experiment on that graph.

Causal Inference

Inferring Graphs from Cascades: A Sparse Recovery Framework

no code implementations21 May 2015 Jean Pouget-Abadie, Thibaut Horel

In the Network Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph.

Generative Adversarial Nets

1 code implementation NeurIPS 2014 Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: 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. The training procedure for G is to maximize the probability of D making a mistake.

Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

no code implementations WS 2014 Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merrienboer, Kyunghyun Cho, Yoshua Bengio

The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems.

Machine Translation Translation

Generative Adversarial Networks

180 code implementations Proceedings of the 27th International Conference on Neural Information Processing Systems 2014 Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: 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. The training procedure for G is to maximize the probability of D making a mistake.

Super-Resolution Time-Series Few-Shot Learning with Heterogeneous Channels

Cannot find the paper you are looking for? You can Submit a new open access paper.