1.3k citations · 4.9k across the 14 of their papers we have counts for
3 papers · 1 filter
Learning to Linearize Under Uncertainty
Ross Goroshin, Michael Mathieu, Yann LeCun
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 whi…
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Pierre Sermanet, David Eigen, Xiang Zhang +3
We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be effi…
Fast Training of Convolutional Networks through FFTs
Michael Mathieu, Mikael Henaff, Yann LeCun
Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability to learn complex functions, lar…