Bayesian & Probabilistic

Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks PGM 2020 Uncertain Natural Language Inference ACL 2020 Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning AAAI 2020 Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation NeurIPS 2020 Uncertainty-Aware Score Distribution Learning for Action Quality Assessment CVPR 2020 Uncertainty-guided Continual Learning with Bayesian Neural Networks ICLR 2020 Uncertainty in Neural Networks: Approximately Bayesian Ensembling AISTATS 2020 Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks COLING 2020 Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery AISTATS 2020 Undirected Graphical Models as Approximate Posteriors ICML 2020 UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses SemEval 2020 Variable Skipping for Autoregressive Range Density Estimation ICML 2020 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020 Variance Reduction in Stochastic Particle-Optimization Sampling ICML 2020 Variational Amodal Object Completion NeurIPS 2020 Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities ICLR 2020 Variational Autoencoders for Sparse and Overdispersed Discrete Data AISTATS 2020 Variational Bayesian Monte Carlo with Noisy Likelihoods NeurIPS 2020 Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings NeurIPS 2020 Variational Pathway Reasoning for EEG Emotion Recognition AAAI 2020 Variational Recurrent Models for Solving Partially Observable Control Tasks ICLR 2020 Variational Template Machine for Data-to-Text Generation ICLR 2020 VFlow: More Expressive Generative Flows with Variational Data Augmentation ICML 2020 Walsh-Hadamard Variational Inference for Bayesian Deep Learning NeurIPS 2020 Wasserstein Control of Mirror Langevin Monte Carlo COLT 2020