variational inference

The Generalized Reparameterization Gradient NeurIPS 2016 Tight Variational Bounds via Random Projections and I-Projections AISTATS 2016 Understanding Probabilistic Sparse Gaussian Process Approximations NeurIPS 2016 Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model NeurIPS 2016 Variational Bayes on Monte Carlo Steroids NeurIPS 2016 Variational Dependent Multi-output Gaussian Process Dynamical Systems JMLR 2016 Variational Gaussian Copula Inference AISTATS 2016 Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes JMLR 2016 Variational Inference for Monte Carlo Objectives ICML 2016 Variational Inference in Mixed Probabilistic Submodular Models NeurIPS 2016 Variational Information Maximization for Feature Selection NeurIPS 2016 Variational Tempering AISTATS 2016 VIME: Variational Information Maximizing Exploration NeurIPS 2016 A Fast Variational Approach for Learning Markov Random Field Language Models ICML 2015 A New Perspective and Extension of the Gaussian Filter RSS 2015 An MRF-Poselets Model for Detecting Highly Articulated Humans ICCV 2015 A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis ICCV 2015 A Recurrent Latent Variable Model for Sequential Data NeurIPS 2015 A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data ICML 2015 Automatic Variational Inference in Stan NeurIPS 2015 Barrier Frank-Wolfe for Marginal Inference NeurIPS 2015 Bayesian Adaptive Matrix Factorization With Automatic Model Selection CVPR 2015 Bayesian dark knowledge NeurIPS 2015 Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) NeurIPS 2015 Bounding errors of Expectation-Propagation NeurIPS 2015