Variational Inference

Stochastic Recurrent Neural Network for Speech Recognition Interspeech 2017 Structured Bayesian Pruning via Log-Normal Multiplicative Noise NeurIPS 2017 Tied Variational Autoencoder Backends for i-Vector Speaker Recognition Interspeech 2017 Tomography of the London Underground: a Scalable Model for Origin-Destination Data NeurIPS 2017 VAE Learning via Stein Variational Gradient Descent NeurIPS 2017 Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold CVPR 2017 Variational Bayesian Multiple Instance Learning With Gaussian Processes CVPR 2017 Variational Inference for Gaussian Process Models with Linear Complexity NeurIPS 2017 Variational Inference via χ Upper Bound Minimization NeurIPS 2017 Variational Policy for Guiding Point Processes ICML 2017 A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models AISTATS 2016 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 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 Disease Trajectory Maps NeurIPS 2016 Distributed Flexible Nonlinear Tensor Factorization NeurIPS 2016 d-VMP: Distributed Variational Message Passing PGM 2016 Early Stopping as Nonparametric Variational Inference AISTATS 2016 Energetic Natural Gradient Descent ICML 2016 Exponential Stochastic Cellular Automata for Massively Parallel Inference AISTATS 2016 Incremental Variational Sparse Gaussian Process Regression NeurIPS 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016