Workshop Proceedings

Listed below are the workshop papers accepted to the International Conference on Learning Representations (ICLR) 2014.

Deep Learning Embeddings for Discontinuous Linguistic Units

    Wenpeng Yin; Hinrich Schütze

Learning Factored Representations in a Deep Mixture of Experts

    David Eigen; Marc'Aurelio Ranzato; Ilya Sutskever

Learning Information Spread in Content Networks

    Cédric Lagnier; Simon Bourigault; Sylvain Lamprier; Ludovic Denoyer; Patrick Gallinari

Learning States Representations in POMDP

    Gabriella Contardo; Ludovic Denoyer; Thierry Artieres; Patrick Gallinari

Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds

    Irina Sergienya; Hinrich Schütze

Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence

    Mathias Berglund; Tapani Raiko

Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error

    David Buchaca; Enrique Romero; Ferran Mazzanti; Jordi Delgado

Continuous Learning: Engineering Super Features With Feature Algebras

    Michael Tetelman

Multimodal Transitions for Generative Stochastic Networks

    Sherjil Ozair; Li Yao; Yoshua Bengio

Factorial Hidden Markov Models for Learning Representations of Natural Language

    Anjan Nepal; Alexander Yates

Can recursive neural tensor networks learn logical reasoning?

    Samuel R. Bowman

A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

    Jianshu Chen; Li Deng

Principled Non-Linear Feature Selection

    Dimitrios Athanasakis; John Shawe-Taylor; Delmiro Fernandez-Reyes

Rate-Distortion Auto-Encoders

    Luis G. Sanchez Giraldo; Jose C. Principe

Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks

    Andrew Davis; Itamar Arel

Semistochastic Quadratic Bound Methods

    Aleksandr Y. Aravkin; Anna Choromanska; Tony Jebara; Dimitri Kanevsky

An Architecture for Distinguishing between Predictors and Inhibitors in Reinforcement Learning

    Patrick C. Connor; Thomas P. Trappenberg

Relaxations for inference in restricted Boltzmann machines

    Sida I. Wang; Roy Frostig; Percy Liang; Christopher D. Manning

Learning Semantic Script Knowledge with Event Embeddings

    Ashutosh Modi; Ivan Titov

Unsupervised Feature Learning by Deep Sparse Coding

    Yunlong He; Koray Kavukcuoglu; Yun Wang; Arthur Szlam; Yanjun Qi

End-to-End Text Recognition with Hybrid HMM Maxout Models

    Ouais Alsharif; Joelle Pineau

Multi-GPU Training of ConvNets

    Omry Yadan; Keith Adams; Yaniv Taigman; Marc'Aurelio Ranzato

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

    Thomas Huang; Zhe Lin; Hailin Jin; Jianchao Yang; Thomas Paine

Generic Deep Networks with Wavelet Scattering

    Edouard Oyallon; Stéphane Mallat; Laurent Sifre

Deep learning for class-generic object detection

    Brody Huval; Adam Coates; Andrew Ng

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

    Karen Simonyan; Andrea Vedaldi; Andrew Zisserman

Unsupervised feature learning by augmenting single images

    Alexey Dosovitskiy; Jost Tobias Springenberg; Thomas Brox

Correlation-based construction of neighborhood and edge features

    Balázs Kégl

Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines

    Nan Wang; Laurenz Wiskott; Dirk Jancke

Understanding Deep Architectures using a Recursive Convolutional Network

    David Eigen; Jason Rolfe; Rob Fergus; Yann LeCun

Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data

    Wei Yu; Tiejun Zhao; Yalong Bai; Wei-Ying Ma; Kuiyuan Yang

One-Shot Adaptation of Supervised Deep Convolutional Models

    Judy Hoffman; Eric Tzeng; Jeff Donahue; Yangqing Jia; Kate Saenko; Trevor Darrell

Improving Deep Neural Networks with Probabilistic Maxout Units

    Jost Tobias Springenberg; Martin Riedmiller

Efficient Visual Coding: From Retina To V2

    Honghao Shan; Garrison Cottrell

Deep learning for neuroimaging: a validation study

    Sergey M. Plis; Devon R. Hjelm; Ruslan Salakhutdinov; Vince D. Calhoun

Image Representation Learning Using Graph Regularized Auto-Encoders

    Yiyi Liao; Yue Wang; Yong Liu

Deep Belief Networks for Image Denoising

    Mohammad Ali Keyvanrad; Mohammad Pezeshki; Mohammad Mehdi Homayounpour

Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images

    Taichi Kiwaki; Takaki Makino; Kazuyuki Aihara