Learning to Linearize Under Uncertainty
Annual Conference on Neural Information Processing Systems, 2015.
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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