893 citations · 2.3k across the 7 of their papers we have counts for
5 papers · 1 filter
Disentangling factors of variation in deep representations using adversarial training
Michael Mathieu, Junbo Zhao, Pablo Sprechmann +2
We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary c…
Energy-based Generative Adversarial Network
Junbo Zhao, Michael Mathieu, Yann LeCun
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near th…
The Loss Surfaces of Multilayer Networks
Anna Choromanska, Mikael Henaff, Michael Mathieu +2
We study the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-g…
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
Nicolas Vasilache, Jeff Johnson, Michael Mathieu +3
We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transfo…
Fast Approximation of Rotations and Hessians matrices
Michael Mathieu, Yann LeCun
A new method to represent and approximate rotation matrices is introduced. The method represents approximations of a rotation matrix with linearithmic complexity, i.e. with $\f…