Bayesian & Probabilistic

Learning Stochastic Feedforward Neural Networks NeurIPS 2013 Learning Stochastic Inverses NeurIPS 2013 Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models ICML 2013 Learning the Local Statistics of Optical Flow NeurIPS 2013 Learning the Structure of Sum-Product Networks ICML 2013 Learning the Visual Interpretation of Sentences ICCV 2013 Learning to Pass Expectation Propagation Messages NeurIPS 2013 Lexical and Hierarchical Topic Regression NeurIPS 2013 Locally Adaptive Bayesian Multivariate Time Series NeurIPS 2013 Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization CVPR 2013 Low-rank matrix reconstruction and clustering via approximate message passing NeurIPS 2013 Machine Teaching for Bayesian Learners in the Exponential Family NeurIPS 2013 Manipulation Pattern Discovery: A Nonparametric Bayesian Approach ICCV 2013 Marginals-to-Models Reducibility NeurIPS 2013 Memoized Online Variational Inference for Dirichlet Process Mixture Models NeurIPS 2013 Message Passing Inference with Chemical Reaction Networks NeurIPS 2013 Message passing with l1 penalized KL minimization ICML 2013 Mixture of Mutually Exciting Processes for Viral Diffusion ICML 2013 Modeling Clutter Perception using Parametric Proto-object Partitioning NeurIPS 2013 Modeling Overlapping Communities with Node Popularities NeurIPS 2013 Modeling Temporal Evolution and Multiscale Structure in Networks ICML 2013 (More) Efficient Reinforcement Learning via Posterior Sampling NeurIPS 2013 Multi-class Video Co-segmentation with a Generative Multi-video Model CVPR 2013 Multi-Prediction Deep Boltzmann Machines NeurIPS 2013 Multiscale Dictionary Learning for Estimating Conditional Distributions NeurIPS 2013