ICLR 2013

55 papers

A Nested HDP for Hierarchical Topic Models John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
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A Semantic Matching Energy Function for Learning with Multi-Relational Data Xavier Glorot, Antoine Bordes, Jason Weston, Yoshua Bengio
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Adaptive Learning Rates and Parallelization for Stochastic, Sparse, Non-Smooth Gradients Tom Schaul, Yann LeCun
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Affinity Weighted Embedding Jason Weston, Ron J. Weiss, Hector Yee
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Auto-Pooling: Learning to Improve Invariance of Image Features from Image Sequences Sainbayar Sukhbaatar, Takaki Makino, Kazuyuki Aihara
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Barnes-Hut-SNE Laurens van der Maaten
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Big Neural Networks Waste Capacity Yann N. Dauphin, Yoshua Bengio
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Block Coordinate Descent for Sparse NMF Vamsi K. Potluru, Sergey M. Plis, Jonathan Le Roux, Barak A. Pearlmutter, Vince D. Calhoun, Thomas P. Hayes
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Boltzmann Machines and Denoising Autoencoders for Image Denoising Kyunghyun Cho
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Clustering Learning for Robotic Vision Eugenio Culurciello, Jordan Bates, Aysegul Dundar, José Antonio Pérez-Carrasco, Clément Farabet
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Complexity of Representation and Inference in Compositional Models with Part Sharing Alan L. Yuille, Roozbeh Mottaghi
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Cutting Recursive Autoencoder Trees Christian Scheible, Hinrich Schütze
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Deep Learning for Detecting Robotic Grasps Ian Lenz, Honglak Lee, Ashutosh Saxena
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Deep Predictive Coding Networks Rakesh Chalasani, José C. Príncipe
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Discrete Restricted Boltzmann Machines Guido Montúfar, Jason Morton
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Discriminative Recurrent Sparse Auto-Encoders Jason Tyler Rolfe, Yann LeCun
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Efficient Estimation of Word Representations in Vector Space Tomás Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
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Efficient Learning of Domain-Invariant Image Representations Judy Hoffman, Erik Rodner, Jeff Donahue, Kate Saenko, Trevor Darrell
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Factorized Topic Models Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström
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Feature Grouping from Spatially Constrained Multiplicative Interaction Felix Bauer, Roland Memisevic
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Feature Learning in Deep Neural Networks - A Study on Speech Recognition Tasks Dong Yu, Michael L. Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide
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Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models Derek C. Rose, Itamar Arel
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Herded Gibbs Sampling Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling
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Hierarchical Data Representation Model - Multi-Layer NMF Hyun Ah Song, Soo-Young Lee
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Indoor Semantic Segmentation Using Depth Information Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun
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Information Theoretic Learning with Infinitely Divisible Kernels Luis Gonzalo Sánchez Giraldo, José C. Príncipe
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Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals Sebastian Hitziger, Maureen Clerc, Alexandre Gramfort, Sandrine Saillet, Christian G. Bénar, Théodore Papadopoulo
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Joint Training Deep Boltzmann Machines for Classification Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio
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Knowledge Matters: Importance of Prior Information for Optimization Çaglar Gülçehre, Yoshua Bengio
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Latent Relation Representations for Universal Schemas Sebastian Riedel, Limin Yao, Andrew McCallum
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Learnable Pooling Regions for Image Classification Mateusz Malinowski, Mario Fritz
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Learning Features with Structure-Adapting Multi-View Exponential Family Harmoniums Yoonseop Kang, Seungjin Choi
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Learning New Facts from Knowledge Bases with Neural Tensor Networks and Semantic Word Vectors Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng
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Learning Stable Group Invariant Representations with Convolutional Networks Joan Bruna, Arthur Szlam, Yann LeCun
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Linear-Nonlinear-Poisson Neurons Can Do Inference on Deep Boltzmann Machines Louis Yuanlong Shao
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Local Component Analysis Nicolas Le Roux, Francis R. Bach
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Matrix Approximation Under Local Low-Rank Assumption Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer
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Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines Guillaume Desjardins, Razvan Pascanu, Aaron C. Courville, Yoshua Bengio
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Natural Gradient Revisited Razvan Pascanu, Yoshua Bengio
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Pushing Stochastic Gradient Towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities Tommi Vatanen, Tapani Raiko, Harri Valpola, Yann LeCun
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Regularized Auto-Encoders Estimate Local Statistics Guillaume Alain, Yoshua Bengio, Salah Rifai
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Regularized Discriminant Embedding for Visual Descriptor Learning Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi
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Saturating Auto-Encoder Rostislav Goroshin, Yann LeCun
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Stochastic Pooling for Regularization of Deep Convolutional Neural Networks Matthew D. Zeiler, Rob Fergus
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The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization Hugo Van hamme
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The Manifold of Human Emotions Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan A. Essa
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The Neural Representation Benchmark and Its Evaluation on Brain and Machine Charles F. Cadieu, Ha Hong, Dan Yamins, Nicolas Pinto, Najib J. Majaj, James J. DiCarlo
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Training Neural Networks with Stochastic Hessian-Free Optimization Ryan Kiros
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Tree Structured Sparse Coding on Cubes Arthur Szlam
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Two SVDs Produce More Focal Deep Learning Representations Hinrich Schütze, Christian Scheible
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Unsupervised Feature Learning for Low-Level Local Image Descriptors Christian Osendorfer, Justin Bayer, Patrick van der Smagt
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Visual Objects Classification with Sliding Spatial Pyramid Matching Hao Wooi Lim, Yong Haur Tay
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When Does a Mixture of Products Contain a Product of Mixtures? Guido F. Montúfar, Jason Morton
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Why Size Matters: Feature Coding as Nystrom Sampling Oriol Vinyals, Yangqing Jia, Trevor Darrell
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Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
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