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Conference Program

Thursday May 2:

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  

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

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


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

Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities

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

Natural Gradient Revisited

Razvan Pascanu, Yoshua Bengio

Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums

Yoonseop Kang, Seungjin Choi