Conference Proceedings

Listed below are the conference papers accepted to the International Conference on Learning Representations (ICLR) 2013.

Discrete Restricted Boltzmann Machines [video]

  Guido F. Montufar, Jason Morton

Feature grouping from spatially constrained multiplicative interaction [video]

  Felix Bauer, Roland Memisevic

Efficient Learning of Domain-invariant Image Representations[video]

              Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, Kate Saenko

Indoor Semantic Segmentation using depth information [video]

  Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun

The Neural Representation Benchmark and its Evaluation on Brain and Machine [video]

  Charles F. Cadieu, Ha Hong, Dan Yamins, Nicolas Pinto, Najib J. Majaj, James J. DiCarlo

Feature Learning in Deep Neural Networks - A Study on Speech Recognition Tasks [video]

              Dong Yu, Michael L. Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide

Barnes-Hut-SNE [video]

  Laurens van der Maaten

Herded Gibbs Sampling [video]


               Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling

Information Theoretic Learning with Infinitely Divisible Kernels [video]

   Luis G. Sanchez Giraldo, Jose C. Principe

What Regularized Auto-Encoders Learn from the Data Generating Distribution [video]

   Guillaume Alain, Yoshua Bengio

Discriminative Recurrent Sparse Auto-Encoders [video]

   Jason Tyler Rolfe, Yann LeCun

Complexity of Representation and Inference in Compositional Models with Part Sharing [video]

   Alan L. Yuille, Roozbeh Mottaghi

Stochastic Pooling for Regularization of Deep Convolutional Neural Networks [video]

   Matthew D. Zeiler, Rob Fergus

Knowledge Matters: Importance of Prior Information for Optimization [video]

   Caglar Gulcehre, Yoshua Bengio

Local Component Analysis

                            Nicolas Le Roux, Francis Bach

The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization

                           Hugo Van hamme

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

                            Sebastian Hitziger, Maureen Clerc, Alexandre Gramfort, Sandrine Saillet, Christian Bénar, Théodore Papadopoulo

Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients

                            Tom Schaul, Yann LeCun

Training Neural Networks with Stochastic Hessian-Free Optimization

                            Ryan Kiros

Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines

                            Guillaume Desjardins, Razvan Pascanu, Aaron Courville, Yoshua Bengio

Block Coordinate Descent for Sparse NMF

                            Vamsi K. Potluru, Sergey M. Plis, Jonathan Le Roux, Barak A. Pearlmutter, Vince D. Calhoun, Thomas P. Hayes

Cutting Recursive Autoencoder Trees

                            Christian Scheible, Hinrich Schuetze

Saturating Auto-Encoder

                            Rostislav Goroshin, Yann LeCun

Factorized Topic Models

                            Cheng Zhang, Carl Henrik Ek, Hedvig Kjellstrom