List of accepted papers:

• “Autoencoders, Unsupervised Learning, and Deep Atchitectures“, Pierre Baldi
• “Dyna Planning using a Feature Based Generative Model“,  Ryan Faulkner, Doina Precup
• “Dynamic Auto-Encoders for Semantic Indexing”,  Piotr Mirowski, Marc’Aurelio Ranzato, Yann LeCun
• “Deep Networks with Hierarchical Convolutional Factor Analysis”, Bo Chen, Guillermo Sapiro, David Dunson, Lawrence Carin
• “Modeling V1 and V2 by multinomial multilayer belief net“, Haruo Hosoya
• “Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks“, Richard Socher, Andrew Ng, Christopher Manning
• “Sum-Product Networks: A New Deep Architecture“, Hoifung Poon, Pedro Domingos
• “Mixture Models and Representational Power of RBM’s, DBN’s and DBM’s“, Guido Montufar
• “Investigating Convergence of Restricted Boltzmann Machine Learning“, Hannes Schulz, Andreas Mueller, Sven Behnke
• “Biasing Restricted Boltzmann Machines to Manipulate Latent Selectivity and Sparsity“, Hanlin Goh, Nicolas Thome and Matthieu Cord
• “Deep Self-Taught Learning for Handwritten Character Recognition“, Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger-Lewandowski, Thomas Breuel, Youssouf Chherawala, Moustapha Cisse, Myriam Côté, Dumitru Erhan, Jeremy Eustache, Xavier Glorot, Xavier Muller, Sylvain Pannetier Lebeuf, Razvan Pascanu, Salah Rifai, François Savard, Guillaume Sicard
• “Rates-FPCD: A Better-Mixing Sampling Procedure for RBMs“, Olivier Breuleux, Yoshua Bengio, Pascal Vincent
• “Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition“, Dong Yu, Li Deng, George Dahl
• “Deep Sparse Rectifier Neural Networks“, Xavier Glorot, Antoine Bordes, Yoshua Bengio
• “Important Gains from Supervised Fine-Tuning of Deep Architectures on Large Labeled Sets“, Pascal Lamblin, Yoshua Bengio
• “A Probabilistic Model for Semantic Word Vectors“, Andrew Maas, Andrew Ng
• “On random weights and unsupervised feature learning” (supplementary material), Andrew Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Ng
• “Deep Learning of Invariant Spatio-Temporal Features from Video“, Bo Chen, Jo-Anne Ting, Benjamin Marlin, Nando de Freitas
• “Learning attentional mechanisms for simultaneous object tracking and recognition with deep networks”, Loris Bazzani, Nando de Freitas, Jo-Anne Ting
• “Multimodal Deep Learning“, Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Ng
• “The spike and slab restricted Boltzmann machine“, Aaron Courville, James Bergstra, Yoshua Bengio
• “Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs“, Guillaume Desjardins, Aaron Courville, Yoshua Bengio
• “An Analysis of Single-Layer Networks in Unsupervised Feature Learning“, Adam Coates, Honglak Lee, Andrew Ng
• “Deep Learning for Efficient Discriminative Parsing“, Ronan Collobert