Variational Inference

Parametric Gaussian Process Regressors ICML 2020 Probabilistic Circuits for Variational Inference in Discrete Graphical Models NeurIPS 2020 Probabilistic Graphical Models with Neural Networks in InferPy PGM 2020 Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations NeurIPS 2020 Relative gradient optimization of the Jacobian term in unsupervised deep learning NeurIPS 2020 Residual Neural Processes AAAI 2020 Robust, Accurate Stochastic Optimization for Variational Inference NeurIPS 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020 Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression AAAI 2020 Sparse Orthogonal Variational Inference for Gaussian Processes AISTATS 2020 Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes AAAI 2020 Spike and slab variational Bayes for high dimensional logistic regression NeurIPS 2020 Stein Self-Repulsive Dynamics: Benefits From Past Samples NeurIPS 2020 Stochastic Convolutional Recurrent Networks for Language Modeling Interspeech 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Stochastic Stein Discrepancies NeurIPS 2020 Switching Poisson Gamma Dynamical Systems IJCAI 2020 Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020 The Autoencoding Variational Autoencoder NeurIPS 2020 The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks ICML 2020 Thompson Sampling via Local Uncertainty ICML 2020 Towards Consistent Variational Auto-Encoding (Student Abstract) AAAI 2020 Towards Interpretable Natural Language Understanding with Explanations as Latent Variables NeurIPS 2020 Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020