Models

Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent NeurIPS 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 Non-conjugate Variational Message Passing for Multinomial and Binary Regression NeurIPS 2011 Online Variational Inference for the Hierarchical Dirichlet Process AISTATS 2011 On Tracking The Partition Function NeurIPS 2011 Practical Variational Inference for Neural Networks NeurIPS 2011 Probabilistic Joint Image Segmentation and Labeling NeurIPS 2011 Robust Bayesian Matrix Factorisation AISTATS 2011 Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets ACML 2011 Select and Sample - A Model of Efficient Neural Inference and Learning NeurIPS 2011 t-divergence Based Approximate Inference NeurIPS 2011 The Neural Autoregressive Distribution Estimator AISTATS 2011 Two Distributed-State Models For Generating High-Dimensional Time Series JMLR 2011 Variational Gaussian Process Dynamical Systems NeurIPS 2011 Variational Learning for Recurrent Spiking Networks NeurIPS 2011 Approximate Inference by Compilation to Arithmetic Circuits NeurIPS 2010 Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes JMLR 2010 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