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- Ian J. Goodfellow, Jean Pouget-Abadie, +5 authors Yoshua Bengio
- NIPS
- 2014

We propose a new framework for estimating generative models via an adversar-ial 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… (More)

- Ian J. Goodfellow, Jean Pouget-Abadie, +5 authors Yoshua Bengio
- ArXiv
- 2014

We propose a new framework for estimating generative models via an adversar-ial 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… (More)

- Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merrienboer, Kyunghyun Cho, Yoshua Bengio
- SSST@EMNLP
- 2014

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. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases… (More)

- Jean Pouget-Abadie, Thibaut Horel
- ICML
- 2015

In the Graph Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. We approach this problem from the sparse recovery perspective. We introduce a general model of cascades, including the voter model and the independent cascade model, for which we provide the first algorithm which… (More)

In this lecture, we are going to cover 'Online algorithms' which study decision-making in the face of uncertainty. We suppose there is some cost to be paid for making a decision. At the end of a sequence of requests, we want to make sure we didn't spend much more than the least we could've spent in hindsight. Definition 1. Let OPT be the algorithm that… (More)

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