From the 1 of 140 linked papers with an AI index.
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Universal halting times in optimization and machine learning
Levent Sagun, Thomas Trogdon, Yann LeCun
The authors present empirical distributions for the halting time (measured by the number of iterations to reach a given accuracy) of optimization algorithms applied to two random s…
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 multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, Yann LeCun
Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some d…
Binary embeddings with structured hashed projections
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski +3
We consider the hashing mechanism for constructing binary embeddings, that involves pseudo-random projections followed by nonlinear (sign function) mappings. The pseudo-random proj…
Universum Prescription: Regularization using Unlabeled Data
Xiang Zhang, Yann LeCun
This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning. We call it universum prescripti…
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Jure Žbontar, Yann LeCun
We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation.…