Brian Cheung, Ph D Student, UC Berkeley Deep Learning Summit 2015

This presentation took place at the Deep Learning Summit in San Francisco on 2930 January 2015. Supervised learning algorithms attempt to learn task relevant factors while being invariant to all others. In contrast, unsupervised learning algorithms seek latent factors which are relevant for a wide range of high level tasks. In this work, we combine these two ideas by augmenting autoencoders with a supervised learning cost to create a semisuper