14.5k citations · 48.8k across the 185 of their papers we have counts for
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Does Neural Machine Translation Benefit from Larger Context?
Sebastien Jean, Stanislas Lauly, Orhan Firat +1
We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence. These models lead to better performance, both in terms of…
Boundary-Seeking Generative Adversarial Networks
R Devon Hjelm, Athul Paul Jacob, Tong Che +3
Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributio…
Describing Videos by Exploiting Temporal Structure
Li Yao, Atousa Torabi, Kyunghyun Cho +4
Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are…
On the Equivalence Between Deep NADE and Generative Stochastic Networks
Li Yao, Sherjil Ozair, Kyunghyun Cho +1
Neural Autoregressive Distribution Estimators (NADEs) have recently been shown as successful alternatives for modeling high dimensional multimodal distributions. One issue associat…
Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep Learning
Kyunghyun Cho, Yoshua Bengio
Many state-of-the-art results obtained with deep networks are achieved with the largest models that could be trained, and if more computation power was available, we might be able…
Iterative Neural Autoregressive Distribution Estimator (NADE-k)
Tapani Raiko, Li Yao, Kyunghyun Cho +1
Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data. We propose a new model that ext…