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.
3. Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling, 2018
: Sampling free uncertainty estimation using mixture density networks.
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.
5. Evidential Deep Learning to Quantify ClassificationUncertainty, 2018
: Similart to 'Predictive Uncertainty Estimation via Prior Networks'
6. Novel Uncertainty Framework for Deep Learning Ensembles, 2019
: Ensemble of classifiers
7. Generative Probabilistic Novelty Detection withAdversarial Autoencoders, 2018
: Novelty detection (using reconstruction loss)