Deep Learning

A Novel Two-Step Method for Cross Language Representation Learning NeurIPS 2013 Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs NeurIPS 2013 Approximate Dynamic Programming Finally Performs Well in the Game of Tetris NeurIPS 2013 Approximate inference in latent Gaussian-Markov models from continuous time observations NeurIPS 2013 A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks NeurIPS 2013 A Sentence Is Worth a Thousand Pixels CVPR 2013 Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop CVPR 2013 Attribute Dominance: What Pops Out? ICCV 2013 Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling CVPR 2013 Bayesian Canonical Correlation Analysis JMLR 2013 Bellman Error Based Feature Generation using Random Projections on Sparse Spaces NeurIPS 2013 Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition ICCV 2013 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 Binary Code Ranking with Weighted Hamming Distance CVPR 2013 Blind Calibration in Compressed Sensing using Message Passing Algorithms NeurIPS 2013 Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF) CVPR 2013 BRDF Slices: Accurate Adaptive Anisotropic Appearance Acquisition CVPR 2013 Capacity of strong attractor patterns to model behavioural and cognitive prototypes NeurIPS 2013 Capturing Global Semantic Relationships for Facial Action Unit Recognition ICCV 2013 Cartesian K-Means CVPR 2013 Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models CVPR 2013 Characterizing Layouts of Outdoor Scenes Using Spatial Topic Processes ICCV 2013 Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories CVPR 2013 Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels ACML 2013 Compete to Compute NeurIPS 2013