uncertainty quantification

Confidence Modeling for Neural Semantic Parsing ACL 2018 Constraining the Dynamics of Deep Probabilistic Models ICML 2018 Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning ICML 2018 Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification NeurIPS 2018 Discriminative Learning of Prediction Intervals AISTATS 2018 Evidential Deep Learning to Quantify Classification Uncertainty NeurIPS 2018 Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam ICML 2018 Gaussian Process Neural Networks for Speech Recognition Interspeech 2018 Handling Uncertainty in Recommender Systems under the Belief Function Theory IJCAI 2018 High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach ICML 2018 Implicit Probabilistic Integrators for ODEs NeurIPS 2018 Including Uncertainty when Learning from Human Corrections CoRL 2018 Large-Scale Distance Metric Learning With Uncertainty CVPR 2018 Learning Structural Weight Uncertainty for Sequential Decision-Making AISTATS 2018 Learning under Misspecified Objective Spaces CoRL 2018 Lightweight Probabilistic Deep Networks CVPR 2018 Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics CVPR 2018 Optimization over Continuous and Multi-dimensional Decisions with Observational Data NeurIPS 2018 Predictive Uncertainty Estimation via Prior Networks NeurIPS 2018 Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures ACL 2018 Structured Uncertainty Prediction Networks CVPR 2018 The Power of Ensembles for Active Learning in Image Classification CVPR 2018 Uncertainty-Aware Attention for Reliable Interpretation and Prediction NeurIPS 2018 Wrapped Gaussian Process Regression on Riemannian Manifolds CVPR 2018 Active Exploration for Learning Symbolic Representations NeurIPS 2017