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Uncertainty Quantification

746 papers

Papers per year

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68
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Papers

Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction CVPR 2019
Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences CVPR 2019
Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera CVPR 2019
Modelling Uncertainty in Collaborative Document Quality Assessment EMNLP 2019
Quantifying Uncertainty in Online Regression Forests JMLR 2019
Noise Contrastive Priors for Functional Uncertainty UAI 2019
Implicit Probabilistic Integrators for ODEs NIPS 2018
Predictive Uncertainty Estimation via Prior Networks NIPS 2018
To Trust Or Not To Trust A Classifier NIPS 2018
Learning Confidence Sets using Support Vector Machines NIPS 2018
Evidential Deep Learning to Quantify Classification Uncertainty NIPS 2018
Reward Estimation for Variance Reduction in Deep Reinforcement Learning CORL 2018
Learning under Misspecified Objective Spaces CORL 2018
Discriminative Learning of Prediction Intervals AISTATS 2018
Learning Structural Weight Uncertainty for Sequential Decision-Making AISTATS 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression ICML 2018
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings ICML 2018
The Uncertainty Bellman Equation and Exploration ICML 2018
Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty IJCAI 2018
Selective Classification for Deep Neural Networks NIPS 2017
Concrete Dropout NIPS 2017
Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning CORL 2017
Incorporating Uncertainty into Deep Learning for Spoken Language Assessment ACL 2017
Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models RSS 2017
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests JMLR 2016