14.5k citations · 90.1k across the 347 of their papers we have counts for
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Deep Directed Generative Autoencoders
Sherjil Ozair, Yoshua Bengio
For discrete data, the likelihood can be rewritten exactly and parametrized into if has enough capacity to put no pro…
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Stephan Gouws, Yoshua Bengio, Greg Corrado
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can…
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…
On the Number of Linear Regions of Deep Neural Networks
Guido Montúfar, Razvan Pascanu, Kyunghyun Cho +1
We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions th…