37 citations · 54 across the 5 of their papers we have counts for
5 papers
Neural Network Regularization via Robust Weight Factorization
Jan Rudy, Weiguang Ding, Daniel Jiwoong Im +1
Regularization is essential when training large neural networks. As deep neural networks can be mathematically interpreted as universal function approximators, they are effective a…
Generative Class-conditional Autoencoders
Jan Rudy, Graham Taylor
Recent work by Bengio et al. (2013) proposes a sampling procedure for denoising autoencoders which involves learning the transition operator of a Markov chain. The transition opera…
Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics
Daniel Jiwoong Im, Ethan Buchman, Graham W. Taylor
Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Ma…
Theano-based Large-Scale Visual Recognition with Multiple GPUs
Weiguang Ding, Ruoyan Wang, Fei Mao +1
In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs. Our performance on 2 GPUs is comparable…
Products of Hidden Markov Models: It Takes N>1 to Tango
Graham W Taylor, Geoffrey E. Hinton
Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to thei…