Models

Image Denoising and Inpainting with Deep Neural Networks NeurIPS 2012 Learning Algorithms for the Classification Restricted Boltzmann Machine JMLR 2012 Learning as MAP Inference in Discrete Graphical Models NeurIPS 2012 Learning the Architecture of Sum-Product Networks Using Clustering on Variables NeurIPS 2012 Learning visual motion in recurrent neural networks NeurIPS 2012 Meta-Gaussian Information Bottleneck NeurIPS 2012 Multimodal Learning with Deep Boltzmann Machines NeurIPS 2012 Multiple Texture Boltzmann Machines AISTATS 2012 Perfect Dimensionality Recovery by Variational Bayesian PCA NeurIPS 2012 Phoneme Classification using Constrained Variational Gaussian Process Dynamical System NeurIPS 2012 Priors for Diversity in Generative Latent Variable Models NeurIPS 2012 Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models NeurIPS 2012 The variational hierarchical EM algorithm for clustering hidden Markov models NeurIPS 2012 Training sparse natural image models with a fast Gibbs sampler of an extended state space NeurIPS 2012 Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes NeurIPS 2012 Truncation-free Online Variational Inference for Bayesian Nonparametric Models NeurIPS 2012 Variational Inference for Crowdsourcing NeurIPS 2012 Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding NeurIPS 2012 Approximate Marginals in Latent Gaussian Models JMLR 2011 Discussion of “The Neural Autoregressive Distribution Estimator” AISTATS 2011 EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning NeurIPS 2011 Expressive Power and Approximation Errors of Restricted Boltzmann Machines NeurIPS 2011 Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines NeurIPS 2011 Generalized Beta Mixtures of Gaussians NeurIPS 2011 Generative Kernels for Exponential Families AISTATS 2011