76 citations · 211 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
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…
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs
Guillaume Desjardins, Aaron Courville, Yoshua Bengio
Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, bec…