uncertainty estimation

Multi-Hypothesis Machine Translation Evaluation ACL 2020 NGBoost: Natural Gradient Boosting for Probabilistic Prediction ICML 2020 Non-Parametric Calibration for Classification AISTATS 2020 Online Learning in Contextual Bandits using Gated Linear Networks NeurIPS 2020 On Power Laws in Deep Ensembles NeurIPS 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks NeurIPS 2020 On the Uncertainty of Self-Supervised Monocular Depth Estimation CVPR 2020 Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning ICLR 2020 Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts NeurIPS 2020 Predictive Uncertainty Estimation for Tractable Deep Probabilistic Models IJCAI 2020 Probabilistic Regression for Visual Tracking CVPR 2020 Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract) AAAI 2020 Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons AAAI 2020 Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness NeurIPS 2020 Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples NeurIPS 2020 Towards More Accurate Uncertainty Estimation In Text Classification EMNLP 2020 Training Binary Neural Networks using the Bayesian Learning Rule ICML 2020 Trust the Model When It Is Confident: Masked Model-based Actor-Critic NeurIPS 2020 Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents AAAI 2020 Uncertainty-Aware Curriculum Learning for Neural Machine Translation ACL 2020 Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation NeurIPS 2020 Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification AAAI 2020 Uncertainty-guided Continual Learning with Bayesian Neural Networks ICLR 2020 Understanding and Exploring the Network with Stochastic Architectures NeurIPS 2020 Variational Inference for Computational Imaging Inverse Problems JMLR 2020