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ORCIDICLR Workshop Track 2013: Scottsdale, AZ, USA
- Yoshua Bengio, Yann LeCun:
1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings. 2013
Oral Presentation
- Oriol Vinyals, Yangqing Jia, Trevor Darrell:
Why Size Matters: Feature Coding as Nystrom Sampling. - Jason Weston, Ron J. Weiss, Hector Yee:
Affinity Weighted Embedding. - Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng:
Zero-Shot Learning Through Cross-Modal Transfer. - Yann N. Dauphin, Yoshua Bengio:
Big Neural Networks Waste Capacity. - Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Joint Training Deep Boltzmann Machines for Classification. - John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan:
A Nested HDP for Hierarchical Topic Models. - Ian Lenz, Honglak Lee, Ashutosh Saxena:
Deep Learning for Detecting Robotic Grasps.
Poster Presentation
- Hyun Ah Song, Soo-Young Lee:
Hierarchical Data Representation Model - Multi-layer NMF. - Sebastian Riedel, Limin Yao, Andrew McCallum:
Latent Relation Representations for Universal Schemas. - Christian Osendorfer, Justin Bayer, Patrick van der Smagt:
Unsupervised Feature Learning for low-level Local Image Descriptors. - Hao Wooi Lim, Yong Haur Tay:
Visual Objects Classification with Sliding Spatial Pyramid Matching. - Joan Bruna, Arthur Szlam, Yann LeCun:
Learning Stable Group Invariant Representations with Convolutional Networks. - Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi:
Regularized Discriminant Embedding for Visual Descriptor Learning. - Hinrich Schütze, Christian Scheible:
Two SVDs produce more focal deep learning representations. - Razvan Pascanu, Yoshua Bengio:
Natural Gradient Revisited. - Mateusz Malinowski, Mario Fritz:
Learnable Pooling Regions for Image Classification. - Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng:
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors. - Derek C. Rose, Itamar Arel:
Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models. - Rakesh Chalasani, José C. Príncipe:
Deep Predictive Coding Networks. - Arthur Szlam:
Tree structured sparse coding on cubes. - Yoonseop Kang, Seungjin Choi:
Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums. - Tomás Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean:
Efficient Estimation of Word Representations in Vector Space. - Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström:
Factorized Topic Models. - Tommi Vatanen, Tapani Raiko, Harri Valpola, Yann LeCun:
Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities. - Kyunghyun Cho:
Boltzmann Machines and Denoising Autoencoders for Image Denoising. - Xavier Glorot, Antoine Bordes, Jason Weston, Yoshua Bengio:
A Semantic Matching Energy Function for Learning with Multi-relational Data. - Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan A. Essa:
The Manifold of Human Emotions. - Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer:
Matrix Approximation under Local Low-Rank Assumption. - Louis Yuanlong Shao:
Linear-Nonlinear-Poisson Neurons Can Do Inference On Deep Boltzmann Machines. - Sainbayar Sukhbaatar, Takaki Makino, Kazuyuki Aihara:
Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences. - Eugenio Culurciello, Jordan Bates, Aysegul Dundar, José Antonio Pérez-Carrasco, Clément Farabet:
Clustering Learning for Robotic Vision. - Guido F. Montúfar, Jason Morton:
When Does a Mixture of Products Contain a Product of Mixtures?
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