Uncertainty-related paper
1. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, 2017
: Infer epistemic uncertainty and aleatoric uncertainty using Bayesian neural networks.
2. Predictive Uncertainty Estimation via Prior Networks, 2018
: Epistemic uncertainty + aleatoric uncertainty + out of distribution estimation using adversarial data.
https://papers.nips.cc/paper/7936-predictive-uncertainty-estimation-via-prior-networks
3. Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling, 2018
: Sampling free uncertainty estimation using mixture density networks.
https://arxiv.org/abs/1709.02249
4. To Trust Or Not To Trust A Classifier, 2018
: Propose thetrust score which measures theagreement between the classifier and a modified nearest-neighbor classifier onthe testing example.
http://papers.nips.cc/paper/7798-to-trust-or-not-to-trust-a-classifier
5. Evidential Deep Learning to Quantify ClassificationUncertainty, 2018
: Similart to 'Predictive Uncertainty Estimation via Prior Networks'
http://papers.nips.cc/paper/7580-evidential-deep-learning-to-quantify-classification-uncertainty
6. Novel Uncertainty Framework for Deep Learning Ensembles, 2019
: Ensemble of classifiers
https://arxiv.org/abs/1904.04917
7. Generative Probabilistic Novelty Detection withAdversarial Autoencoders, 2018
: Novelty detection (using reconstruction loss)