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Super-Resolution with Deep Convolutional Sufficient Statistics
Joan Bruna, Pablo Sprechmann, Yann LeCun
Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the condi…
Deep Convolutional Networks on Graph-Structured Data
Mikael Henaff, Joan Bruna, Yann LeCun
Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationa…
Unsupervised Feature Learning from Temporal Data
Ross Goroshin, Joan Bruna, Jonathan Tompson +2
Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coher…
A mathematical motivation for complex-valued convolutional networks
Joan Bruna, Soumith Chintala, Yann LeCun +3
A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an in…