Variational Inference

Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models NeurIPS 2017 Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems NeurIPS 2017 Fast amortized inference of neural activity from calcium imaging data with variational autoencoders NeurIPS 2017 Fast Black-box Variational Inference through Stochastic Trust-Region Optimization NeurIPS 2017 f-GANs in an Information Geometric Nutshell NeurIPS 2017 Filtering Variational Objectives NeurIPS 2017 From Bayesian Sparsity to Gated Recurrent Nets NeurIPS 2017 Gray-box Inference for Structured Gaussian Process Models AISTATS 2017 Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery Interspeech 2017 Hierarchical Implicit Models and Likelihood-Free Variational Inference NeurIPS 2017 Identification of Gaussian Process State Space Models NeurIPS 2017 Improving Variational Methods via Pairwise Linear Response Identities JMLR 2017 Latent Variable Dialogue Models and their Diversity EACL 2017 Neural Expectation Maximization NeurIPS 2017 On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior AISTATS 2017 Perturbative Black Box Variational Inference NeurIPS 2017 Reducing Reparameterization Gradient Variance NeurIPS 2017 Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017 Scalable Variational Inference for Dynamical Systems NeurIPS 2017 Scalable Variational Inference for Super Resolution Microscopy AISTATS 2017 Speaker Clustering by Iteratively Finding Discriminative Feature Space and Cluster Labels Interspeech 2017 Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences JMLR 2017 Stein Variational Gradient Descent as Gradient Flow NeurIPS 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 Stochastic Gradient MCMC Methods for Hidden Markov Models ICML 2017