76 citations · 124 across the 3 of their papers we have counts for
3 papers
Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines
Guillaume Desjardins, Razvan Pascanu, Aaron Courville +1
This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm…
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…