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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…
Scoring and Classifying with Gated Auto-encoders
Daniel Jiwoong Im, Graham W. Taylor
Auto-encoders are perhaps the best-known non-probabilistic methods for representation learning. They are conceptually simple and easy to train. Recent theoretical work has shed lig…
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…
"Mental Rotation" by Optimizing Transforming Distance
Weiguang Ding, Graham W. Taylor
The human visual system is able to recognize objects despite transformations that can drastically alter their appearance. To this end, much effort has been devoted to the invarianc…