variational inference

Multi-resolution Multi-task Gaussian Processes NeurIPS 2019 Multi-Source Neural Variational Inference AAAI 2019 Multi-task Learning for Aggregated Data using Gaussian Processes NeurIPS 2019 Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks UAI 2019 Neural Gaussian Copula for Variational Autoencoder EMNLP 2019 Neural Spline Flows NeurIPS 2019 Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models ICML 2019 One-Shot Marginal MAP Inference in Markov Random Fields UAI 2019 On Variational Bounds of Mutual Information ICML 2019 Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019 Parametric Manifold Learning of Gaussian Mixture Models IJCAI 2019 Pathwise Derivatives for Multivariate Distributions AISTATS 2019 PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows ICCV 2019 Point-to-Point Video Generation ICCV 2019 Poisson-Randomized Gamma Dynamical Systems NeurIPS 2019 Practical Deep Learning with Bayesian Principles NeurIPS 2019 Privacy-Preserving Variational Information Feature Extraction for Domestic Activity Monitoring versus Speaker Identification Interspeech 2019 Probabilistic Logic Neural Networks for Reasoning NeurIPS 2019 Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning ICLR 2019 Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning ICML 2019 Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions NeurIPS 2019 Provable Gradient Variance Guarantees for Black-Box Variational Inference NeurIPS 2019 Pyro: Deep Universal Probabilistic Programming JMLR 2019 Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization ICML 2019 Random Function Priors for Correlation Modeling ICML 2019