variational inference

NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations AAAI 2020 Neural Methods for Point-wise Dependency Estimation NeurIPS 2020 Neural Mixed Counting Models for Dispersed Topic Discovery ACL 2020 Neural Topic Model with Attention for Supervised Learning AISTATS 2020 Non-convex Learning via Replica Exchange Stochastic Gradient MCMC ICML 2020 Normalizing Flows With Multi-Scale Autoregressive Priors CVPR 2020 OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation UAI 2020 On Implicit Regularization in $β$-VAEs ICML 2020 Online Neural Connectivity Estimation with Noisy Group Testing NeurIPS 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks NeurIPS 2020 Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds NeurIPS 2020 Ordinal Non-negative Matrix Factorization for Recommendation ICML 2020 Parametric Gaussian Process Regressors ICML 2020 Planning from Images with Deep Latent Gaussian Process Dynamics L4DC 2020 Point process models for sequence detection in high-dimensional neural spike trains NeurIPS 2020 Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control ICLR 2020 Probabilistic Circuits for Variational Inference in Discrete Graphical Models NeurIPS 2020 Probabilistic Graphical Models with Neural Networks in InferPy PGM 2020 Probabilistic Pixel-Adaptive Refinement Networks CVPR 2020 Projected Stein Variational Gradient Descent NeurIPS 2020 Provable Smoothness Guarantees for Black-Box Variational Inference ICML 2020 Quantile Graphical Models: a Bayesian Approach JMLR 2020 Quantile Propagation for Wasserstein-Approximate Gaussian Processes NeurIPS 2020 Quantized Variational Inference NeurIPS 2020 Radial and Directional Posteriors for Bayesian Deep Learning AAAI 2020