Models

Variational Gaussian-process factor analysis for modeling spatio-temporal data NeurIPS 2009 Variational Inference for the Nested Chinese Restaurant Process NeurIPS 2009 A Scalable Hierarchical Distributed Language Model NeurIPS 2008 DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification NeurIPS 2008 Evaluating probabilities under high-dimensional latent variable models NeurIPS 2008 Gates NeurIPS 2008 Generative versus discriminative training of RBMs for classification of fMRI images NeurIPS 2008 Implicit Mixtures of Restricted Boltzmann Machines NeurIPS 2008 Maximal Causes for Non-linear Component Extraction JMLR 2008 Sparse probabilistic projections NeurIPS 2008 The Recurrent Temporal Restricted Boltzmann Machine NeurIPS 2008 Variational Mixture of Gaussian Process Experts NeurIPS 2008 Building Blocks for Variational Bayesian Learning of Latent Variable Models JMLR 2007 Collapsed Variational Inference for HDP NeurIPS 2007 Efficient Inference for Distributions on Permutations NeurIPS 2007 Loop Series and Bethe Variational Bounds in Attractive Graphical Models NeurIPS 2007 Modeling image patches with a directed hierarchy of Markov random fields NeurIPS 2007 Neural characterization in partially observed populations of spiking neurons NeurIPS 2007 Sparse deep belief net model for visual area V2 NeurIPS 2007 The Infinite Gamma-Poisson Feature Model NeurIPS 2007 Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes NeurIPS 2007 Variational Inference for Diffusion Processes NeurIPS 2007 Variational inference for Markov jump processes NeurIPS 2007 Accelerated Variational Dirichlet Process Mixtures NeurIPS 2006 A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation NeurIPS 2006