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mathieu

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Nov 10, 2016
By mathieu
Conferences: NIPS
Machine Learning

Disentangling factors of variation in deep representations using ad…

We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and sep…
Sep 11, 2016
Conferences: ICLR
Machine Learning

Energy-based Generative Adversarial Network

We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low …
Nov 17, 2015
By mathieu
Conferences: ICLR
Machine Learning
Computer Vision

Deep multi-scale video prediction beyond mean square error

Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accur…
Jun 09, 2015
Computer Vision

Learning to Linearize Under Uncertainty

Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. H…
Jun 08, 2015
Conferences: ICLR
Machine Learning
Neural and Evolutionary Computation

Stacked What-Where Auto-encoders

We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides…
Nov 30, 2014
Machine Learning

The Loss Surfaces of Multilayer Networks

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 Hami…
Dec 20, 2013
By mathieu
Conferences: ICLR
Computer Vision
Machine Learning
Neural and Evolutionary Computation

Fast Training of Convolutional Networks through FFTs

Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability …
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