08:00 - 09:00 Breakfast Oral session (9:00 - 12:30) 09:00 - 09:40 Invited talk: Recent Applications of Deep Boltzmann Machines [video] Ruslan Salakhutdinov 09:40 - 10:00 Discrete Restricted Boltzmann Machines [video] Guido F. Montufar, Jason Morton 10:00 - 10:20 Feature grouping from spatially constrained multiplicative interaction [video] Felix Bauer, Roland Memisevic 10:20 - 10:50 Break 10:50 - 11:30 Invited talk: Learning Compositional Models [video] Alan Yuille 11:30 - 11:50 Efficient Learning of Domain-invariant Image Representations[video] Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, Kate Saenko 11:50 - 12:10 Indoor Semantic Segmentation using depth information [video] Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun 12:10 - 12:30 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 12:30 - 17:30 Lunch on own / free time 17:30 - 19:00 Dinner 19:00 - 22:00 Poster session I Friday May 3: 08:00 - 09:00 Breakfast Oral session (9:00 - 12:40) 09:00 - 09:40 Invited talk: Deep Learning of Recursive Structure: Grammar Induction [video] Jason Eisner 09:40 - 10:00 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 10:00 - 10:20 Barnes-Hut-SNE [video] Laurens van der Maaten 10:20 - 10:50 Break 10:50 - 11:30 Invited talk: Submodularity and Big Data [video] Jeff Bilmes 11:30 - 11:40 A Nested HDP for Hierarchical Topic Models [video] John Paisley, Chong Wang, David Blei, Michael I. Jordan 11:40 - 11:50 Affinity Weighted Embedding [video] Jason Weston, Ron Weiss, Hector Yee 11:50 - 12:00 Big Neural Networks Waste Capacity [video] Yann N. Dauphin, Yoshua Bengio 12:00 - 12:10 Zero-Shot Learning Through Cross-Modal Transfer [video] Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng 12:10 - 12:20 Why Size Matters: Feature Coding as Nystrom Sampling [video] Oriol Vinyals, Yangqing Jia, Trevor Darrell 12:20 - 12:30 Joint Training Deep Boltzmann Machines for Classification [video] Ian J. Goodfellow, Aaron Courville, Yoshua Bengio 12:30 - 12:40 Deep Learning for Detecting Robotic Grasps [video] Ian Lenz, Honglak Lee, Ashutosh Saxena 12:40 - 17:30 Lunch on own / free time 17:30 - 19:00 Dinner 19:00 - 22:00 Poster session II Saturday May 4: 08:00 - 09:00 Breakfast Oral session (9:00 - 12:30) 09:00 - 09:20 Herded Gibbs Sampling [video] Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling 09:20 - 09:40 Information Theoretic Learning with Infinitely Divisible Kernels [video] Luis G. Sanchez Giraldo, Jose C. Principe 09:40 - 10:00 What Regularized Auto-Encoders Learn from the Data Generating Distribution [video] Guillaume Alain, Yoshua Bengio 10:00 - 10:20 Discriminative Recurrent Sparse Auto-Encoders [video] Jason Tyler Rolfe, Yann LeCun 10:20 - 10:50 Break 10:50 - 11:30 Invited talk: Austerity in MCM-‐Land: Cutting the computational Budget [video] Max Welling 11:30 - 11:50 Complexity of Representation and Inference in Compositional Models with Part Sharing [video] Alan L. Yuille, Roozbeh Mottaghi 11:50 - 12:10 Stochastic Pooling for Regularization of Deep Convolutional Neural Networks [video] Matthew D. Zeiler, Rob Fergus 12:10 - 12:30 Knowledge Matters: Importance of Prior Information for Optimization [video] Caglar Gulcehre, Yoshua Bengio 12:30 - 17:30 Lunch on own / free time 17:30 - 19:20 Banquet 19:30 - 20:10 Invited talk: Drednets Geoffrey Hinton All videos courtesy of techtalks.tv Poster session I (Evening of Thursday May 2nd) Authors with conference orals are also welcome to present a poster in the conference poster session on Thursday evening 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 Rostislav Goroshin, Yann LeCun Cheng Zhang, Carl Henrik Ek, Hedvig Kjellstrom ---------------------------------------- Poster session II (Evening of Friday May 3rd) Authors with workshop orals are also welcome to present a poster in the workshop poster session on Friday evening The Manifold of Human Emotions Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan Essa Two SVDs produce more focal deep learning representations Hinrich Schuetze, Christian Scheible Visual Objects Classification with Sliding Spatial Pyramid Matching Hao Wooi Lim, Yong Haur Tay Learnable Pooling Regions for Image Classification Mateusz Malinowski, Mario Fritz Tommi Vatanen, Tapani Raiko, Harri Valpola, Yann LeCun Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models Derek Rose, Itamar Arel Deep Predictive Coding Networks Rakesh Chalasani, Jose C. Principe Clustering Learning for Robotic Vision Eugenio Culurciello, Jordan Bates, Aysegul Dundar, Jose Carrasco, Clement Farabet Matrix Approximation under Local Low-Rank Assumption Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines Louis Yuanlong Shao Learning Stable Group Invariant Representations with Convolutional Networks Joan Bruna, Arthur Szlam, Yann LeCun Boltzmann Machines and Denoising Autoencoders for Image Denoising Kyunghyun Cho Regularized Discriminant Embedding for Visual Descriptor Learning Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi Hierarchical Data Representation Model - Multi-layer NMF Hyun Ah Song, Soo-Young Lee Efficient Estimation of Word Representations in Vector Space Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean A Semantic Matching Energy Function for Learning with Multi-relational Data Xavier Glorot, Antoine Bordes, Jason Weston, Yoshua Bengio Latent Relation Representations for Universal Schemas Sebastian Riedel, Limin Yao, Andrew McCallum Tree structured sparse coding on cubes Arthur Szlam Razvan Pascanu, Yoshua Bengio Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums Yoonseop Kang, Seungjin Choi |
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