ICLR 2014

73 papers

A Generative Product-of-Filters Model of Audio Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore

A New Method for Learning Deep Recurrent Neural Networks Jianshu Chen, Li Deng

A Simple Model for Learning Multilingual Compositional Semantics Karl Moritz Hermann, Phil Blunsom

An Architecture for Distinguishing Between Predictors and Inhibitors in Reinforcement Learning Patrick C. Connor, Thomas Trappenberg

An Empirical Analysis of Dropout in Piecewise Linear Networks David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio

An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, Yoshua Bengio

Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara

Auto-Encoding Variational Bayes Diederik P. Kingma, Max Welling

Bounding the Test Log-Likelihood of Generative Models Yoshua Bengio, Li Yao

Can Recursive Neural Tensor Networks Learn Logical Reasoning? Samuel R. Bowman

Continuous Learning: Engineering Super Features with Feature Algebras Michael Tetelman

Correlation-Based Construction of Neighborhood and Edge Features Balázs Kégl

Deep and Wide Multiscale Recursive Networks for Robust Image Labeling Gary B. Huang, Viren Jain

Deep Belief Networks for Image Denoising Mohammad Ali Keyvanrad, Mohammad Pezeshki, Mohammad Ali Homayounpour

Deep Convolutional Ranking for Multilabel Image Annotation Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, Sergey Ioffe

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

Deep Learning Embeddings for Discontinuous Linguistic Units Wenpeng Yin, Hinrich Schütze

Deep Learning for Class-Generic Object Detection Brody Huval, Adam Coates, Andrew Y. Ng

Deep Learning for Neuroimaging: A Validation Study Sergey M. Plis, R. Devon Hjelm, Ruslan Salakhutdinov, Vince D. Calhoun

Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds Irina Sergienya, Hinrich Schütze

Efficient Visual Coding: From Retina to V2 Honghao Shan, Garrison W. Cottrell

End-to-End Text Recognition with Hybrid HMM Maxout Models Ouais Alsharif, Joelle Pineau

Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks Andrew M. Saxe, James L. McClelland, Surya Ganguli

EXMOVES: Classifier-Based Features for Scalable Action Recognition Du Tran, Lorenzo Torresani

Factorial Hidden Markov Models for Learning Representations of Natural Language Anjan Nepal, Alexander Yates

Fast Training of Convolutional Networks Through FFTs Michaël Mathieu, Mikael Henaff, Yann LeCun

Generic Deep Networks with Wavelet Scattering Edouard Oyallon, Stéphane Mallat, Laurent Sifre

GPU Asynchronous Stochastic Gradient Descent to Speed up Neural Network Training Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas S. Huang

Group-Sparse Embeddings in Collective Matrix Factorization Arto Klami, Guillaume Bouchard, Abhishek Tripathi

How to Construct Deep Recurrent Neural Networks Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, Yoshua Bengio

Image Representation Learning Using Graph Regularized Auto-Encoders Yiyi Liao, Yue Wang, Yong Liu

Improving Deep Neural Networks with Probabilistic Maxout Units Jost Tobias Springenberg, Martin A. Riedmiller

Intriguing Properties of Neural Networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus

K-Sparse Autoencoders Alireza Makhzani, Brendan J. Frey

Learned Versus Hand-Designed Feature Representations for 3D Agglomeration John A. Bogovic, Gary B. Huang, Viren Jain

Learning Factored Representations in a Deep Mixture of Experts David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever

Learning High-Level Image Representation for Image Retrieval via Multi-Task DNN Using Clickthrough Data Yalong Bai, Kuiyuan Yang, Wei Yu, Wei-Ying Ma, Tiejun Zhao

Learning Human Pose Estimation Features with Convolutional Networks Arjun Jain, Jonathan Tompson, Mykhaylo Andriluka, Graham W. Taylor, Christoph Bregler

Learning Information Spread in Content Networks Cédric Lagnier, Simon Bourigault, Sylvain Lamprier, Ludovic Denoyer, Patrick Gallinari

Learning Non-Linear Feature Maps, with an Application to Representation Learning Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes

Learning Semantic Script Knowledge with Event Embeddings Ashutosh Modi, Ivan Titov

Learning States Representations in POMDP Gabriella Contardo, Ludovic Denoyer, Thierry Artières, Patrick Gallinari

Learning Transformations for Classification Forests Qiang Qiu, Guillermo Sapiro

Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks Andrew S. Davis, Itamar Arel

Modeling Correlations in Spontaneous Activity of Visual Cortex with Centered Gaussian-Binary Deep Boltzmann Machines Nan Wang, Laurenz Wiskott, Dirk Jancke

Multi-Digit Number Recognition from Street View Imagery Using Deep Convolutional Neural Networks Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay D. Shet

Multi-GPU Training of ConvNets Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato

Multi-View Priors for Learning Detectors from Sparse Viewpoint Data Bojan Pepik, Michael Stark, Peter V. Gehler, Bernt Schiele

Multimodal Transitions for Generative Stochastic Networks Sherjil Ozair, Li Yao, Yoshua Bengio

Network in Network Min Lin, Qiang Chen, Shuicheng Yan

Neuronal Synchrony in Complex-Valued Deep Networks David P. Reichert, Thomas Serre

On Fast Dropout and Its Applicability to Recurrent Networks Justin Bayer, Christian Osendorfer, Nutan Chen, Sebastian Urban, Patrick van der Smagt

On the Number of Inference Regions of Deep Feed Forward Networks with Piece-Wise Linear Activations Razvan Pascanu, Guido Montúfar, Yoshua Bengio

One-Shot Adaptation of Supervised Deep Convolutional Models Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell

OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, Yann LeCun

Rate-Distortion Auto-Encoders Luis Gonzalo Sánchez Giraldo, José C. Príncipe

Relaxations for Inference in Restricted Boltzmann Machines Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning

Revisiting Natural Gradient for Deep Networks Razvan Pascanu, Yoshua Bengio

Semistochastic Quadratic Bound Methods for Convex and Nonconvex Learning Problems Aleksandr Y. Aravkin, Anna Choromanska, Dimitri Kanevsky, Tony Jebara

Sequentially Generated Instance-Dependent Image Representations for Classification Gabriel Dulac-Arnold, Ludovic Denoyer, Nicolas Thome, Matthieu Cord, Patrick Gallinari

Some Improvements on Deep Convolutional Neural Network Based Image Classification Andrew G. Howard

Sparse Similarity-Preserving Hashing Jonathan Masci, Alexander M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro

Spectral Networks and Locally Connected Networks on Graphs Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun

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 Prats, Enrique Romero, Ferran Mazzanti, Jordi Delgado

The Return of AdaBoost.MH: Multi-Class Hamming Trees Balázs Kégl

The Role of Spatio-Temporal Synchrony in the Encoding of Motion Kishore Reddy Konda, Roland Memisevic, Vincent Michalski

Understanding Deep Architectures Using a Recursive Convolutional Network David Eigen, Jason Tyler Rolfe, Rob Fergus, Yann LeCun

Unit Tests for Stochastic Optimization Tom Schaul, Ioannis Antonoglou, David Silver

Unsupervised Feature Learning by Augmenting Single Images Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox

Unsupervised Feature Learning by Deep Sparse Coding Yunlong He, Koray Kavukcuoglu, Yun Wang, Arthur Szlam, Yanjun Qi

Zero-Shot Learning and Clustering for Semantic Utterance Classification Yann N. Dauphin, Gökhan Tür, Dilek Hakkani-Tür, Larry P. Heck

Zero-Shot Learning by Convex Combination of Semantic Embeddings Mohammad Norouzi, Tomás Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean