Probabilistic Modeling

Functional form of motion priors in human motion perception NeurIPS 2010 Gated Softmax Classification NeurIPS 2010 Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010 Gaussian Process Preference Elicitation NeurIPS 2010 Gaussian sampling by local perturbations NeurIPS 2010 Generating more realistic images using gated MRF's NeurIPS 2010 Global seismic monitoring as probabilistic inference NeurIPS 2010 Heavy-Tailed Process Priors for Selective Shrinkage NeurIPS 2010 Hierarchical Gaussian Process Regression ACML 2010 HOP-MAP: Efficient Message Passing with High Order Potentials AISTATS 2010 Identifying graph-structured activation patterns in networks NeurIPS 2010 Implicit Differentiation by Perturbation NeurIPS 2010 Implicit encoding of prior probabilities in optimal neural populations NeurIPS 2010 Improvements to the Sequence Memoizer NeurIPS 2010 Improving posterior marginal approximations in latent Gaussian models AISTATS 2010 Inference and Learning in Networks of Queues AISTATS 2010 Inference with Multivariate Heavy-Tails in Linear Models NeurIPS 2010 Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks NeurIPS 2010 Layered image motion with explicit occlusions, temporal consistency, and depth ordering NeurIPS 2010 Learning concept graphs from text with stick-breaking priors NeurIPS 2010 Learning Efficient Markov Networks NeurIPS 2010 Learning Non-Stationary Dynamic Bayesian Networks JMLR 2010 Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors NeurIPS 2010 libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models JMLR 2010 Lifted Inference Seen from the Other Side : The Tractable Features NeurIPS 2010