Workshop Proceedings

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

A Nested HDP for Hierarchical Topic Models [video]

   John Paisley, Chong Wang, David Blei, Michael I. Jordan

Affinity Weighted Embedding [video]

   Jason Weston, Ron Weiss, Hector Yee

Big Neural Networks Waste Capacity [video]

   Yann N. Dauphin, Yoshua Bengio

Zero-Shot Learning Through Cross-Modal Transfer [video]

   Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng

Why Size Matters: Feature Coding as Nystrom Sampling [video]

   Oriol Vinyals, Yangqing Jia, Trevor Darrell

Joint Training Deep Boltzmann Machines for Classification [video]

   Ian J. Goodfellow, Aaron Courville, Yoshua Bengio

Deep Learning for Detecting Robotic Grasps [video]

   Ian Lenz, Honglak Lee, Ashutosh Saxena

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