Generative Models

Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding NeurIPS 2012 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 Generative Kernels for Exponential Families AISTATS 2011 Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities NeurIPS 2011 Learning to Learn with Compound HD Models NeurIPS 2011 Mixed-Variate Restricted Boltzmann Machines ACML 2011 Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability NeurIPS 2011 On Tracking The Partition Function NeurIPS 2011 Robust Bayesian Matrix Factorisation AISTATS 2011 Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets ACML 2011 The Neural Autoregressive Distribution Estimator AISTATS 2011 Two Distributed-State Models For Generating High-Dimensional Time Series JMLR 2011 Efficient Learning of Deep Boltzmann Machines AISTATS 2010 Empirical Risk Minimization with Approximations of Probabilistic Grammars NeurIPS 2010 Expectation Truncation and the Benefits of Preselection In Training Generative Models JMLR 2010 Gated Softmax Classification NeurIPS 2010 Generating more realistic images using gated MRF's NeurIPS 2010 Generative Local Metric Learning for Nearest Neighbor Classification NeurIPS 2010 Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model NeurIPS 2010 Inductive Principles for Restricted Boltzmann Machine Learning AISTATS 2010 Learning to combine foveal glimpses with a third-order Boltzmann machine NeurIPS 2010 Regularized estimation of image statistics by Score Matching NeurIPS 2010