Listed below are the conference papers accepted to the International Conference on Learning Representations (ICLR) 2014. Multilingual Distributed Representations without Word Alignment Karl Moritz Hermann; Phil Blunsom 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 Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Andrew M. Saxe; James L. McClelland; Surya Ganguli Revisiting Natural Gradient for Deep Networks Razvan Pascanu; Yoshua Bengio Tom Schaul; Ioannis Antonoglou; David Silver 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; On Fast Dropout and its Applicability to Recurrent Networks Justin Bayer; Christian Osendorfer; Daniela Korhammer; Nutan Chen; Sebastian Urban; Patrick van der Smagt Min Lin; Qiang Chen; Shuicheng Yan Auto-Encoding Variational Bayes Diederik P. Kingma; Max Welling Group-sparse Embeddings in Collective Matrix Factorization Arto Klami; Guillaume Bouchard; Abhishek Tripathi 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 OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Pierre Sermanet; Rob Fergus; Yann LeCun; Xiang Zhang; David Eigen; Michael Mathieu Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks Ian J. Goodfellow; Yaroslav Bulatov; Julian Ibarz; Sacha Arnoud; Vinay Shet Sequentially Generated Instance-Dependent Image Representations for Classification Ludovic Denoyer; Matthieu Cord; Patrick Gallinari; Nicolas Thome; Gabriel Dulac-Arnold Learned versus Hand-Designed Feature Representations for 3d Agglomeration John A. Bogovic; Gary B. Huang; Viren Jain Spectral Networks and Locally Connected Networks on Graphs Joan Bruna; Wojciech Zaremba; Arthur Szlam; Yann LeCun Sparse similarity-preserving hashing Alex M. Bronstein; Pablo Sprechmann; Michael M. Bronstein; Jonathan Masci; Guillermo Sapiro Learning Transformations for Classification Forests Qiang Qiu; Guillermo Sapiro |
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