Listed below are the conference and workshop papers accepted to the International Conference on Learning Representations (ICLR) 2014. Monday April 14: 08:00 - 09:00 Breakfast - Hawthorne Ballroom 09:00 - 12:30 Oral session - Wildrose Ballroom B&C 09:00 - 09:40 Myths of Representation Learning (invited talk) Rich Sutton 09:40 - 10:00 Multilingual Distributed Representations without Word Alignment Karl Moritz Hermann; Phil Blunsom 10:00 - 10:20 Zero-Shot Learning by Convex Combination of Semantic Embeddings Mohammad Norouzi; Tomas Mikolov; Samy Bengio; Yoram Singer; Jonathon Shlens; Andrea Frome; Greg S. Corrado; Jeffrey Dean 10:20 - 10:50 Break 10:50 - 11:30 Speech Representations: Knowledge or Data? (invited talk) Hynek Hermansky 11:30 - 11:50 Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Andrew M. Saxe; James L. McClelland; Surya Ganguli 11:50 - 12:10 Revisiting Natural Gradient for Deep Networks Razvan Pascanu; Yoshua Bengio Tom Schaul; Ioannis Antonoglou; David Silver 12:30 - 17:30 Lunch on own / free time 17:30 - 19:00 Dinner - Hawthorne Ballroom 19:00 - 22:00 Poster session I - Wildrose Ballroom A Conference Posters: [Note for presenters: Poster board size: 4' x 8'] The return of AdaBoost.MH: multi-class Hamming trees Balázs Kégl Neuronal Synchrony in Complex-Valued Deep Networks David P. Reichert; Thomas Serre Bounding the Test Log-Likelihood of Generative Models Yoshua Bengio; Li Yao; KyungHyun Cho A Generative Product-of-Filters Model of Audio Dawen Liang; Mathew D. Hoffman; Gautham Mysore How to Construct Deep Recurrent Neural Networks Razvan Pascanu; Caglar Gulcehre; Kyunghyun Cho; Yoshua Bengio Zero-Shot Learning and Clustering for Semantic Utterance Classification Yann N. Dauphin; Gokhan Tur; Dilek Hakkani-Tur; Larry Heck An empirical analysis of dropout in piecewise linear networks David Warde-Farley; Ian J. Goodfellow; Aaron Courville; Yoshua Bengio An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks Ian J. Goodfellow; Mehdi Mirza; Da Xiao; Aaron Courville; Yoshua Bengio; Justin Bayer; Christian Osendorfer; Daniela Korhammer; Nutan Chen; Sebastian Urban; Patrick van der Smagt Min Lin; Qiang Chen; Shuicheng Yan Workshop Posters: [Note for presenters: Poster board size: 4' x 8'] 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 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 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 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 Tuesday April 15: 08:00 - 09:00 Breakfast - Hawthorne Ballroom 09:00 - 12:40 Oral session- Wildrose B&C 09:00 - 09:40 Symmetry-Based Learning (invited talk) Pedro Domingos 09:40 - 10:00 Auto-Encoding Variational Bayes Diederik P. Kingma; Max Welling 10:00 - 10:20 Group-sparse Embeddings in Collective Matrix Factorization Arto Klami; Guillaume Bouchard; Abhishek Tripathi 10:20 - 10:50 Break 10:50 - 11:30 Learning Visual Representations at Scale (invited talk) Vincent Vanhoucke 11:30 - 11:45 Relaxations for inference in restricted Boltzmann machines Sida I. Wang; Roy Frostig; Percy Liang; Christopher D. Manning 11:45 - 12:00 Learning Semantic Script Knowledge with Event Embeddings Ashutosh Modi; Ivan Titov 12:00 - 12:15 Unsupervised Feature Learning by Deep Sparse Coding Yunlong He; Koray Kavukcuoglu; Yun Wang; Arthur Szlam; Yanjun Qi 12:15 - 12:30 End-to-End Text Recognition with Hybrid HMM Maxout Models Ouais Alsharif; Joelle Pineau 12:40 - 17:30 Lunch on own / free time 17:30 - 19:00 Dinner - Hawthorne Ballroom 19:00 - 22:00 Poster session II - Wildrose Ballroom A Conference Posters: [Note for presenters: Poster board size: 4' x 8'] Learning Human Pose Estimation Features with Convolutional Networks Ajrun Jain; Jonathan Tompson; Mykhaylo Andriluka; Graham W. Taylor; Christoph Bregler EXMOVES: Classifier-based Features for Scalable Action Recognition Du Tran; Lorenzo Torresani On the number of inference regions of deep feed forward networks with piece-wise linear activations Razvan Pascanu; Guido Montufar; Yoshua Bengio Intriguing properties of neural networks Christian Szegedy; Wojciech Zaremba; Ilya Sutskever; Joan Bruna; Dumitru Erhan; Ian Goodfellow; Rob Fergus Fast Training of Convolutional Networks through FFTs Michael Mathieu; Mikael Henaff; Yann LeCun Deep and Wide Multiscale Recursive Networks for Robust Image Labeling Gary B. Huang; Viren Jain Some Improvements on Deep Convolutional Neural Network Based Image Classification Andrew G. Howard Deep Convolutional Ranking for Multilabel Image Annotation Yunchao Gong; Yangqing Jia; Thomas Leung; Alexander Toshev; Sergey Ioffe Learning to encode motion using spatio-temporal synchrony Kishore Reddy Konda; Roland Memisevic; Vincent Michalski Multi-View Priors for Learning Detectors from Sparse Viewpoint Data Bojan Pepik; Michael Stark; Peter Gehler; Bernt Schiele Alireza Makhzani; Brendan Frey Workshop Posters: [Note for presenters: Poster board size: 4' x 8'] 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 Nan Wang; Laurenz Wiskott; Dirk Jancke Understanding Deep Architectures using a Recursive Convolutional Network David Eigen; Jason Rolfe; Rob Fergus; Yann LeCun 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 Wednesday April 16: 08:00 - 09:00 Breakfast - Hawthorne Ballroom 09:00 - 12:30 Oral session - Wildrose Ballroom B&C
09:00 - 09:20 OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Pierre Sermanet; Rob Fergus; Yann LeCun; Xiang Zhang; David Eigen; Michael Mathieu 09:20 - 09:40 Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks Ian J. Goodfellow; Yaroslav Bulatov; Julian Ibarz; Sacha Arnoud; Vinay Shet 09:40 - 10:00 Sequentially Generated Instance-Dependent Image Representations for Classification Ludovic Denoyer; Matthieu Cord; Patrick Gallinari; Nicolas Thome; Gabriel Dulac-Arnold 10:00 - 10:20 Learned versus Hand-Designed Feature Representations for 3d Agglomeration John A. Bogovic; Gary B. Huang; Viren Jain 10:20 - 10:50 Break 10:50 - 11:30 Representing Relations (invited talk) Roland Memisevic 11:30 - 11:50 Spectral Networks and Locally Connected Networks on Graphs Joan Bruna; Wojciech Zaremba; Arthur Szlam; Yann LeCun 11:50 - 12:10 Sparse similarity-preserving hashing Alex M. Bronstein; Pablo Sprechmann; Michael M. Bronstein; Jonathan Masci; Guillermo Sapiro 12:10 - 12:30 Learning Transformations for Classification Forests Qiang Qiu; Guillermo Sapiro 12:30 - 17:30 Lunch on own / free time 17:30 - 19:20 Banquet - Hawthorne Ballroom 19:30 - 20:10 (Town-Hall meeting) - Hawthorne Ballroom
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