POSTER SESSION A: Thursday April 14, 19:30
| 168 | Traffic Signs and Pedestrians Vision with Multi-Scale Convolutional Networks Pierre Sermanet and Koray Kavukcuoglu and Yann LeCun |
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| 163 | Shallow vs. Deep Sum-Product Networks Yoshua Bengio and Olivier Delalleau |
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| 158 | Contracting Auto-Encoders Salah Rifai and Yoshua Bengio and Xavier Muller |
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| 151 | Segmenting low-level instructions into high-level instructions Amit Goyal and Jiarong Jiang and Hal Daume III |
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| 144 | Complex Activity Recognition using Granger Constrained Dynamic Bayesian Network Eran Swears and Anthony Hoogs |
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| 141 | An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling Zhihua Zhang and Dakan Wang |
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| 139 | Asymptotic Performance Guarantee for Online Reinforcement Learning with the Least-Squares Regression Mohammad Gheshlaghi Azar and Hilbert J. Kappen |
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| 138 | Examining Loss Functions for Cost-Sensitive Learning Jacek P. Dmochowski and Paul Sajda and Lucas C. Parra |
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| 137 | Constrained Regression for 3D Pose Estimation Aydin Varol and Mathieu Salzmann and Pascal Fua and Raquel Urtasun |
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| 134 | Compact Belief Propagation for Protein Folding Jian Peng, Tamir Hazan, David McAllester, Raquel Urtasun |
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| 131 | Imitation Learning in Relational Domains Using Functional Gradient Boosting Sriraam Natarajan and Saket Joshi and Prasad Tadepalli and Kristian Kersting and Jude Shavlik |
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| 129 | The Complex Wave Representation (CWR) of Shape Karthik S. Gurumoorthy and Anand Rangarajan and Arunava Banerjee |
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| 127 | Lossy Conservative Update sketch Amit Goyal and Hal Daume III |
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| 126 | Relabelling MCMC Algorithms in Bayesian Mixture Learning Remi Bardenet and Balazs Kegl and Gersende Fort |
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| 119 | Bumps, Blind Source Separation, Synchrony and Alzheimer’s F. B. Vialatte and J. Dauwels and G. Dreyfus |
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| 114 | Structured sparse coding with a quadratic or bilinear penalty Karol Gregor and Arthur Szlam and Yann LeCun |
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| 112 | Self-Terminating Induction of Multiclass Trees Sally Goldman and Yoram Singer |
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| 111 | An Energy-Based Recurrent Neural Network for Multiple Fundamental Frequency Estimation Nicolas Boulanger-Lewandowski and Pascal Vincent and Yoshua Bengio |
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| 109 | Enhanced Gradient for Learning Boltzmann Machines Tapani Raiko and KyungHyun Cho and Alexander Ilin |
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| 103 | Partial least squares based speaker recognition system Balaji Vasan Srinivasan, Dmitry Zotkin and Ramani Duraiswami |
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| 170 | Weighted Co-regularization for Multiview Spectral Clustering Jagadees Jagarlamudi, Abhishek Kumar, Hal Daumé III, Piyush Rai |
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POSTER SESSION B: Friday April 15, 19:30
| 166 | Learning from weak teachers Shai Ben-David, Ruth Urner, Phil Long and Ohad shamir |
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| 161 | Anomaly detection from videos under sparse data and partial observations Sangmin Oh and Anthony Hoogs |
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| 157 | Population MCMC for Dirichlet Diffusion Trees Pu Wang and Kathryn B. Laskey and Carlotta Domeniconi |
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| 156 | BSD: Bit string decomposition of weighted networks Hossein Azari Soufiani and Edoardo M. Airoldi |
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| 153 | Towards Zero-Data Learning of Bayesian Networks Diane Oyen and Terran Lane |
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| 150 | Unifying Manifold Learning Algorithms through the Riemannian Metric Dominique Perrault-Joncas and Marina Meila |
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| 146 | Bayesian inference for identifying interaction rules in animal swarms Richard Mann and Roman Garnett and David Sumpter and Jeff Schneider |
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| 145 | Learning to deal with long-term dependencies Pascanu, R. and Bengio, Y. |
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| 142 | Induction of Composite Features via Grouping and Composition For Image Classification Omid Madani and Brian Burns |
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| 136 | Large Scale Structured Prediction with Hidden Variables Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun |
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| 133 | Learning Dynamic Models from Non-sequenced Data Tzu-Kuo Huang and Jeff Schneider |
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| 132 | Scaling up Inverse Reinforcement Learning through Instructed Feature Construction Tomas Singliar and Dragos D. Margineantu |
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| 130 | Joint Blind Source Separation for Multi-modality Data Fusion through Subject Co-variations Matthew Anderson and Nicolle Correa and Vince D. Calhoun and Tulay Adali |
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| 128 | An Iterated Graph Laplacian Approach for Ranking on Manifolds Xueyuan Zhou and Mikhail Belkin and Nathan Srebro |
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| 118 | Learning low rank matrices online using retracions Uri Shalit and Daphna Weinshall and Gal Chechik |
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| 116 | Variational Empirical Bernstein Boosting Pannagadatta Shivaswamy and Tony Jebara |
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| 115 | Geometric Embedding for Learning Combinatorial Structures Terran Lane and Ben Yackley and Sergey Plis and Stephen McCracken and Blake Anderson |
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| 113 | Nonparametric Divergence Estimation for Learning Manifolds of Distributions and Group Anomaly Detection Barnabas Poczos and Liang Xiong and Jeff Schneider |
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| 110 | Image Retrieval with Multinomial Relevance Feedback Dorota Glowacka and John Shawe-Taylor |
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| 108 | Practical Analysis of the Universum SVM Learning Vladimir Cherkassky and Sauptik Dhar |
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| 104 | Clustering Protein Sequences With Limited Distance Information Konstantin Voevodski and Maria-Florina Balcan and Heiko Roglin and Shang-Hua Teng and Yu Xia |
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| 169 | Generalization of CCA via Spectral Embedding Jagadees Jagarlamudi, Raghavendra Udupa, Hal Daumé III |
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