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ORCIDICLR 2013: Scottsdale, AZ, USA
- Yoshua Bengio, Yann LeCun:
1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Conference Track Proceedings. 2013
Oral Presentation
- Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. - Çaglar Gülçehre, Yoshua Bengio:
Knowledge Matters: Importance of Prior Information for Optimization. - Charles F. Cadieu, Ha Hong, Dan Yamins, Nicolas Pinto, Najib J. Majaj, James J. DiCarlo:
The Neural Representation Benchmark and its Evaluation on Brain and Machine. - Felix Bauer, Roland Memisevic:
Feature grouping from spatially constrained multiplicative interaction. - Jason Tyler Rolfe, Yann LeCun:
Discriminative Recurrent Sparse Auto-Encoders. - Guido Montúfar, Jason Morton:
Discrete Restricted Boltzmann Machines. - Camille Couprie, Clément Farabet, Laurent Najman
, Yann LeCun:
Indoor Semantic Segmentation using depth information. - Matthew D. Zeiler, Rob Fergus:
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. - Luis Gonzalo Sánchez Giraldo, José C. Príncipe:
Information Theoretic Learning with Infinitely Divisible Kernels. - Guillaume Alain, Yoshua Bengio, Salah Rifai:
Regularized Auto-Encoders Estimate Local Statistics. - Alan L. Yuille, Roozbeh Mottaghi:
Complexity of Representation and Inference in Compositional Models with Part Sharing. - Dong Yu, Michael L. Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide:
Feature Learning in Deep Neural Networks - A Study on Speech Recognition Tasks. - Laurens van der Maaten:
Barnes-Hut-SNE.
Poster Presentation
- Judy Hoffman, Erik Rodner, Jeff Donahue, Kate Saenko, Trevor Darrell:
Efficient Learning of Domain-invariant Image Representations. - Christian Scheible, Hinrich Schütze:
Cutting Recursive Autoencoder Trees. - Rostislav Goroshin, Yann LeCun:
Saturating Auto-Encoder. - Sebastian Hitziger, Maureen Clerc, Alexandre Gramfort, Sandrine Saillet, Christian G. Bénar, Théodore Papadopoulo:
Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals. - Ryan Kiros:
Training Neural Networks with Stochastic Hessian-Free Optimization. - Guillaume Desjardins, Razvan Pascanu, Aaron C. Courville, Yoshua Bengio:
Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines. - Tom Schaul, Yann LeCun:
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients. - Vamsi K. Potluru, Sergey M. Plis, Jonathan Le Roux, Barak A. Pearlmutter, Vince D. Calhoun, Thomas P. Hayes:
Block Coordinate Descent for Sparse NMF. - Hugo Van hamme:
The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization. - Nicolas Le Roux, Francis R. Bach:
Local Component Analysis.
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