POSTER SESSION A: Wednesday April 7, 19:30
| 167 | Sparse Rectifier Neural Networks Xavier Glorot and Yoshua Bengio |
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| 166 | Sparse Coding by Bayesian Variational Marginalization Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergus, Yann LeCun |
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| 165 | Relevance Networks for Cold Start Inference of User Preferences Claudiu Branzan and Vlad Iovanov and Oliver Downs |
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| 164 | Minimum Probability Flow Learning Jascha Sohl-Dickstein, Peter Battaglino, Michael R. DeWeese |
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| 163 | AutoMLP: Simple, Effective, Fully Automated Learning Rate and Size Adjustment Thomas Breuel and Faisal Shafait |
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| 160 | Object Recognition by Ranking Figure-Ground Hypotheses Joao Carreira and Fuxin Li and Cristian Sminchisescu |
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| 159 | Convex Multiple-Instance Learning Fuxin Li and Cristian Sminchisescu |
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| 158 | Capturing Broad Temporal Dependencies in Deep-Layer Networks Itamar Arel, Tom Karnowski, Derek Rose |
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| 157 | Unsupervised Learning of Functional Scene Elements in Video Scenes Matt Turek and Anthony Hoogs and Roderic Collins |
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| 156 | Co-regularization Based Analysis of Feature Sharing Algorithms Abhishek Kumar and Avishek Saha and Hal Daumé III and Tom Fletcher and Suresh Venkatasubramanian |
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| 147 | Approximate inference for the loss-calibrated Bayesian Simon Lacoste-Julien and Zoubin Ghahramani |
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| 145 | Reinforcement Planning: Planners as Policies Matt Zucker and J. Andrew Bagnell |
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| 142 | Learning Temporal Causal Graphs for Relational Time-Series Analysis Yan Liu and Alexandru Niculescu-Mizil and Aurelie Lozano |
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| 141 | Incremental Multi-Dimensional Scaling Arvind Agarwal and Jeff M. Phillips and Hal Daumé III and Suresh Venkatasubramanian |
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| 140 | Multitask Learning using Transformation Functions Arvind Agarwal and Hal Daumé III |
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| 128 | Deep Transfer: A Markov Logic Approach Jesse Davis and Pedro Domingos |
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| 127 | A Bound on Log Likelihood from Lyapunov Exponents Andrew M. Fraser |
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| 126 | Inverse Reinforcement Learning with PI^2 Mrinal Kalakrishnan and Evangelos Theodorou and Stefan Schaal |
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| 104 | Deconvolutional Networks for Feature Learning Matt Zeiler, Dilip Krishnan, Graham Taylor, Rob Fergus |
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| 123 | Towards Learning Risk Estimation Functions for Access Control Luke Dickens and Pau-Chen Cheng and Jorge Lobo and Alessandra Russo |
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POSTER SESSION B: Thursday April 8, 19:30
| 155 | Content-based Retrieval of Functional Objects in Video using Scene Context Sang Min Oh and Anthony Hoogs |
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| 125 | Image Domain Adaptation Using Metric Learning Kate Saenko and Brian Kulis and Mario Fritz and Trevor Darrell |
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| 154 | A Connection Between Importance Sampling and Likelihood Ratio Policy Gradients Jie Tang and Pieter Abbeel |
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| 152 | Translating Part-of-Speech Tags via Dependency Structure Adam R. Teichert and Jagadeesh Jagarlamudi and Hal Daumé III |
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| 150 | Convolutional $K$-SVD Arthur Szlam and Koray Kavukcuoglu and Yann LeCun |
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| 149 | Message-Passing Algorithm for Marginal-MAP Estimation Jiarong Jiang and Piyush Rai and Hal Daumé III |
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| 139 | Fast approximate prediction of sparse codes Karol Gregor and Yann LeCun |
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| 138 | Learning to rank semantically relevant video thumbnails Gal Chechik and Tomas Izo and Samy Bengio |
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| 136 | Multi-Agent Inverse Reinforcement Learning Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting and Jude Shavlik |
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| 133 | A Constrained Combination of Discriminative and Generative Methods Mathieu Salzmann and Raquel Urtasun |
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| 129 | Nonparametric Bayesian Methods for Relational Clustering Pu Wang and Kathryn B. Laskey and Carlotta Domeniconi |
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| 122 | Self-Pruning Prediction Trees Sally Goldman and Yoram Singer |
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| 119 | Learning a Parametric Mapping for Non-linear Dimensionality Reduction Pooyan Khajehpour and Ali Ghodsi |
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| 118 | Learning Smoothly Varying Bayesian Network Structures Under Similar Contexts Diane Oyen and Terran Lane |
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| 117 | Fast and Scalable Manifold Learning by Semi-Definite Programming Babak Alipanahi and Nathan Krislocky and Ali Ghodsi |
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| 114 | Unsupervised Language Learning: Semantic Parsing and Beyond Hoifung Poon and Pedro Domingos |
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| 113 | A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning Joe Suzuki |
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| 110 | Evidence Marshalling Andrew M. Fraser |
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| 109 | An Estimation Method for Bradley-Terry and its Related Models based on the Bregman Divergence Yu Fujimoto and Hideitsu Hino and Noboru Murata |
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| 108 | Virtual K-Fold Cross Validation: A Computation-Aware Method For Accuracy Assessment Cesare Alippi and Manuel Rover |
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| 102 | Learning a Multi-Modal Similarity Metric with Application to 2D-3D Matching Raia Hadsell, Bogdan Matei Harpreet Sawhney |
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