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ICLR Workshop 2014: Banff, AB, Canada
- Yoshua Bengio, Yann LeCun:
2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Workshop Track Proceedings. 2014
Oral Presentations
- Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning:
Relaxations for inference in restricted Boltzmann machines. - Ashutosh Modi, Ivan Titov:
Learning Semantic Script Knowledge with Event Embeddings. - Yunlong He, Koray Kavukcuoglu, Yun Wang, Arthur Szlam, Yanjun Qi:
Unsupervised Feature Learning by Deep Sparse Coding. - Ouais Alsharif, Joelle Pineau:
End-to-End Text Recognition with Hybrid HMM Maxout Models.
Poster Presentations
- Wenpeng Yin, Hinrich Schütze:
Deep Learning Embeddings for Discontinuous Linguistic Units. - David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever:
Learning Factored Representations in a Deep Mixture of Experts. - Cédric Lagnier, Simon Bourigault, Sylvain Lamprier, Ludovic Denoyer, Patrick Gallinari:
Learning Information Spread in Content Networks. - Gabriella Contardo, Ludovic Denoyer, Thierry Artières, Patrick Gallinari:
Learning States Representations in POMDP. - Irina Sergienya, Hinrich Schütze:
Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds. - Mathias Berglund, Tapani Raiko:
Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence. - David Buchaca Prats
, Enrique Romero, Ferran Mazzanti, Jordi Delgado
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error. - Michael Tetelman:
Continuous Learning: Engineering Super Features With Feature Algebras. - Sherjil Ozair, Li Yao, Yoshua Bengio:
Multimodal Transitions for Generative Stochastic Networks. - Anjan Nepal, Alexander Yates:
Factorial Hidden Markov Models for Learning Representations of Natural Language. - Samuel R. Bowman:
Can recursive neural tensor networks learn logical reasoning? - Jianshu Chen, Li Deng:
A New Method for Learning Deep Recurrent Neural Networks. - Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes:
Learning Non-Linear Feature Maps, With An Application To Representation Learning. - Luis Gonzalo Sánchez Giraldo, José C. Príncipe:
Rate-Distortion Auto-Encoders. - Andrew S. Davis, Itamar Arel:
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks. - Aleksandr Y. Aravkin, Anna Choromanska, Dimitri Kanevsky, Tony Jebara:
Semistochastic Quadratic Bound Methods for Convex and Nonconvex Learning Problems. - Patrick C. Connor, Thomas Trappenberg:
An Architecture for Distinguishing between Predictors and Inhibitors in Reinforcement Learning. - Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato:
Multi-GPU Training of ConvNets. - Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas S. Huang:
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training. - Edouard Oyallon, Stéphane Mallat, Laurent Sifre:
Generic Deep Networks with Wavelet Scattering. - Brody Huval, Adam Coates, Andrew Y. Ng:
Deep learning for class-generic object detection. - Karen Simonyan, Andrea Vedaldi, Andrew Zisserman:
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. - Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox:
Unsupervised feature learning by augmenting single images. - Balázs Kégl:
Correlation-based construction of neighborhood and edge features. - Nan Wang, Laurenz Wiskott, Dirk Jancke:
Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines. - David Eigen, Jason Tyler Rolfe, Rob Fergus, Yann LeCun:
Understanding Deep Architectures using a Recursive Convolutional Network. - Yalong Bai, Kuiyuan Yang, Wei Yu, Wei-Ying Ma, Tiejun Zhao:
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data. - Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell:
One-Shot Adaptation of Supervised Deep Convolutional Models. - Jost Tobias Springenberg, Martin A. Riedmiller:
Improving Deep Neural Networks with Probabilistic Maxout Units. - Honghao Shan, Garrison W. Cottrell:
Efficient Visual Coding: From Retina To V2. - Sergey M. Plis, R. Devon Hjelm, Ruslan Salakhutdinov, Vince D. Calhoun:
Deep learning for neuroimaging: a validation study. - Yiyi Liao, Yue Wang, Yong Liu:
Image Representation Learning Using Graph Regularized Auto-Encoders. - Mohammad Ali Keyvanrad, Mohammad Pezeshki, Mohammad Ali Homayounpour:
Deep Belief Networks for Image Denoising. - Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara:
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images.
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