7.4k citations · 24.2k across the 136 of their papers we have counts for
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Joint Training of Deep Boltzmann Machines
Ian Goodfellow, Aaron Courville, Yoshua Bengio
We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at…
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions
Heng Luo, Pierre Luc Carrier, Aaron Courville +1
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-th…
Disentangling Factors of Variation via Generative Entangling
Guillaume Desjardins, Aaron Courville, Yoshua Bengio
Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzman…
Efficient EM Training of Gaussian Mixtures with Missing Data
Olivier Delalleau, Aaron Courville, Yoshua Bengio
In data-mining applications, we are frequently faced with a large fraction of missing entries in the data matrix, which is problematic for most discriminant machine learning algori…
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding
Ian Goodfellow, Aaron Courville, Yoshua Bengio
We consider the problem of object recognition with a large number of classes. In order to overcome the low amount of labeled examples available in this setting, we introduce a new…
Representation Learning: A Review and New Perspectives
Yoshua Bengio, Aaron Courville, Pascal Vincent
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or…