7.8k citations · 9.6k across the 18 of their papers we have counts for
4 papers · 1 filter
Understanding disentangling in -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal +4
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we s…
Natural Neural Networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu +1
We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representation during training to improve conditioning 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…
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…