variational inference

Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods NeurIPS 2016 A Theoretically Grounded Application of Dropout in Recurrent Neural Networks NeurIPS 2016 Auxiliary Deep Generative Models ICML 2016 A Variational Analysis of Stochastic Gradient Algorithms ICML 2016 A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes JMLR 2016 Black-Box Alpha Divergence Minimization ICML 2016 Black-Box Policy Search with Probabilistic Programs AISTATS 2016 Clamping Improves TRW and Mean Field Approximations AISTATS 2016 Collapsed Variational Inference for Sum-Product Networks ICML 2016 Composing graphical models with neural networks for structured representations and fast inference NeurIPS 2016 Conditional Graphical Lasso for Multi-Label Image Classification CVPR 2016 Cooperative Graphical Models NeurIPS 2016 Determinantal Regularization for Ensemble Variable Selection AISTATS 2016 Disease Trajectory Maps NeurIPS 2016 Distributed Flexible Nonlinear Tensor Factorization NeurIPS 2016 Diversity-Promoting Bayesian Learning of Latent Variable Models ICML 2016 d-VMP: Distributed Variational Message Passing PGM 2016 Early Stopping as Nonparametric Variational Inference AISTATS 2016 Extended and Unscented Kitchen Sinks ICML 2016 Factor Analysis Based Speaker Verification Using ASR Interspeech 2016 From Noise Modeling to Blind Image Denoising CVPR 2016 Geometric Dirichlet Means Algorithm for topic inference NeurIPS 2016 Globally Sparse Probabilistic PCA AISTATS 2016 Incremental Variational Sparse Gaussian Process Regression NeurIPS 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016