Discovery of Deep Structure from Unlabeled Data
Final rept. 15 Mar 2010-16 Jun 2014
LELAND STANFORD JUNIOR UNIV CA
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This research project addressed the problem of learning useful deep representations from unlabeled data. The major goal was to innovate new unsupervised deep learning algorithms capable of learning important semantic structure in the input data in a domain general way. At the conclusion of this project, these goals stand fulfilled. The lab produced a variety of new and influential learning algorithms including Independent Subspace Analysis ISA Reconstruction Independent Components Analysis RICA recursive neural networks and recursive tensor networks, among others. These algorithms have posted state-of-the-art results across a number of domains and tasks, and have had impact on both academia and industry.