Learning to Linearize Under Uncertainty

Annual Conference on Neural Information Processing Systems, 2015.

Cited by: 95|Bibtex|Views128
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Other Links: academic.microsoft.com|dblp.uni-trier.de|dl.acm.org|arxiv.org

Abstract:

Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in which to train such hierarchies in the unsupervised setting has remained elusive. In this work we suggest a new architecture and loss for training deep feature hie...More

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