https://github.com/ENSTA-U2IS-AI/awesome-uncertainty-deeplearning

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
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This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

• Host: GitHub
• URL: https://github.com/ENSTA-U2IS-AI/awesome-uncertainty-deeplearning
• Owner: ENSTA-U2IS-AI
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README

        
# Awesome Uncertainty in Deep learning



[![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



This repo is a collection of *awesome* papers, codes, books, and blogs about Uncertainty and Deep learning. 

:star: Feel free to star and fork. :star:

If you think we missed a paper, please open a pull request or send a message on the corresponding [GitHub discussion](https://github.com/ENSTA-U2IS-AI/awesome-uncertainty-deeplearning/discussions). Tell us where the article was published and when, and send us GitHub and ArXiv links if they are available.

We are also open to any ideas for improvements!



Table of Contents



- [Awesome Uncertainty in Deep learning](#awesome-uncertainty-in-deep-learning)

- [Papers](#papers)

  - [Surveys](#surveys)

  - [Theory](#theory)

  - [Bayesian-Methods](#bayesian-methods)

  - [Ensemble-Methods](#ensemble-methods)

  - [Sampling/Dropout-based-Methods](#samplingdropout-based-methods)

  - [Post-hoc-Methods/Auxiliary-Networks](#post-hoc-methodsauxiliary-networks)

  - [Data-augmentation/Generation-based-methods](#data-augmentationgeneration-based-methods)

  - [Output-Space-Modeling/Evidential-deep-learning](#output-space-modelingevidential-deep-learning)

  - [Deterministic-Uncertainty-Methods](#deterministic-uncertainty-methods)

  - [Quantile-Regression/Predicted-Intervals](#quantile-regressionpredicted-intervals)

  - [Conformal Predictions](#conformal-predictions)

  - [Calibration/Evaluation-Metrics](#calibrationevaluation-metrics)

  - [Misclassification Detection \& Selective Classification](#misclassification-detection--selective-classification)

  - [Applications](#applications)

    - [Classification and Semantic-Segmentation](#classification-and-semantic-segmentation)

    - [Regression](#regression)

    - [Anomaly-detection and Out-of-Distribution-Detection](#anomaly-detection-and-out-of-distribution-detection)

    - [Object detection](#object-detection)

    - [Domain adaptation](#domain-adaptation)

    - [Semi-supervised](#semi-supervised)

    - [Natural Language Processing](#natural-language-processing)

    - [Others](#others)

- [Datasets and Benchmarks](#datasets-and-benchmarks)

- [Libraries](#libraries)

  - [Python](#python)

  - [PyTorch](#pytorch)

  - [JAX](#jax)

  - [TensorFlow](#tensorflow)

- [Lectures and tutorials](#lectures-and-tutorials)

- [Books](#books)

- [Other Resources](#other-resources)

# Papers

## Surveys

**Conference**

- A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [[AISafety Workshop 2020]]()

**Journal**

- A survey of uncertainty in deep neural networks [[Artificial Intelligence Review 2023]]() - [[GitHub]]() 

- Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation [[TMLR2023]]()

- A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective [[ACM2021]]()

- Ensemble deep learning: A review [[Engineering Applications of AI 2021]]()

- A review of uncertainty quantification in deep learning: Techniques, applications and challenges [[Information Fusion 2021]]()

- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods [[Machine Learning 2021]]()

- Predictive inference with the jackknife+ [[The Annals of Statistics 2021]]()

- Uncertainty in big data analytics: survey, opportunities, and challenges [[Journal of Big Data 2019]]()

**Arxiv**

- Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks [[ArXiv2024]]() - [[PyTorch]]()

- A System-Level View on Out-of-Distribution Data in Robotics [[arXiv2022]]()

- A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning [[arXiv2022]]()

## Theory

**Conference**

- A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods [[NeurIPS2023]]()

- Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning [[ICLR2023]]()

- Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? [[ICLR2023]]()

- Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs [[ICML2023]]() - [[PyTorch]]()

- On Second-Order Scoring Rules for Epistemic Uncertainty Quantification [[ICML2023]]()

- Neural Variational Gradient Descent [[AABI2022]]()

- Top-label calibration and multiclass-to-binary reductions [[ICLR2022]]()

- Bayesian Model Selection, the Marginal Likelihood, and Generalization [[ICML2022]]()

- With malice towards none: Assessing uncertainty via equalized coverage [[AIES 2021]]()

- Uncertainty in Gradient Boosting via Ensembles [[ICLR2021]]() - [[PyTorch]]()

- Repulsive Deep Ensembles are Bayesian [[NeurIPS2021]]() - [[PyTorch]]()

- Bayesian Optimization with High-Dimensional Outputs [[NeurIPS2021]]()

- Residual Pathway Priors for Soft Equivariance Constraints [[NeurIPS2021]]()

- Dangers of Bayesian Model Averaging under Covariate Shift [[NeurIPS2021]]() - [[TensorFlow]]()

- A Mathematical Analysis of Learning Loss for Active Learning in Regression [[CVPR Workshop2021]]()

- Why Are Bootstrapped Deep Ensembles Not Better? [[NeurIPS Workshop]]()

- Deep Convolutional Networks as shallow Gaussian Processes [[ICLR2019]]()

- On the accuracy of influence functions for measuring group effects [[NeurIPS2018]]()

- To Trust Or Not To Trust A Classifier [[NeurIPS2018]]() - [[Python]]()

- Understanding Measures of Uncertainty for Adversarial Example Detection [[UAI2018]]()

**Journal**

- Martingale posterior distributions [[Royal Statistical Society Series B]]()

- A Unified Theory of Diversity in Ensemble Learning [[JMLR2023]]()

- Multivariate Uncertainty in Deep Learning [[TNNLS2021]]()

- A General Framework for Uncertainty Estimation in Deep Learning [[RAL2020]]()

- Adaptive nonparametric confidence sets [[Ann. Statist. 2006]]()

**Arxiv**

- Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping [[arXiv2022]]()

- Efficient Gaussian Neural Processes for Regression [[arXiv2021]]()

- Dense Uncertainty Estimation [[arXiv2021]]() - [[PyTorch]]()

- A higher-order swiss army infinitesimal jackknife [[arXiv2019]]()

## Bayesian-Methods

**Conference**

- Training Bayesian Neural Networks with Sparse Subspace Variational Inference [[ICLR2024]]()

- Variational Bayesian Last Layers [[ICLR2024]](https://arxiv.org/abs/2404.11599)

- A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors [[ICLR2024]]()

- Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning [[CVPR2023]]()

- Robustness to corruption in pre-trained Bayesian neural networks [[ICLR2023]]()

- Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift [[NeurIPS2023]]() - [[PyTorch]]()

- Transformers Can Do Bayesian Inference [[ICLR2022]]() - [[PyTorch]]()

- Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture [[NeurIPS2022]]()

- On Batch Normalisation for Approximate Bayesian Inference [[AABI2021]]()

- Activation-level uncertainty in deep neural networks [[ICLR2021]]()

- Laplace Redux – Effortless Bayesian Deep Learning [[NeurIPS2021]]() - [[PyTorch]]()

- On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks [[UAI2021]]()

- Learnable uncertainty under Laplace approximations [[UAI2021]]()

- Bayesian Neural Networks with Soft Evidence [[ICML Workshop2021]]() - [[PyTorch]]()

- TRADI: Tracking deep neural network weight distributions for uncertainty estimation [[ECCV2020]]() - [[PyTorch]]()

- How Good is the Bayes Posterior in Deep Neural Networks Really? [[ICML2020]]()

- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [[ICML2020]]() - [[TensorFlow]]()

- Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks [[ICML2020]]() - [[PyTorch]]()

- Bayesian Deep Learning and a Probabilistic Perspective of Generalization [[NeurIPS2020]]()

- A Simple Baseline for Bayesian Uncertainty in Deep Learning [[NeurIPS2019]]() - [[PyTorch]]() - [[TorchUncertainty]]()

- Bayesian Uncertainty Estimation for Batch Normalized Deep Networks [[ICML2018]]() - [[TensorFlow]]() - [[TorchUncertainty]]()

- Lightweight Probabilistic Deep Networks [[CVPR2018]]() - [[PyTorch]]()

- A Scalable Laplace Approximation for Neural Networks [[ICLR2018]]() - [[Theano]]()

- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [[ICML2018]]()

- Weight Uncertainty in Neural Networks [[ICML2015]]()

**Journal**

- Analytically Tractable Hidden-States Inference in Bayesian Neural Networks [[JMLR2024]](https://jmlr.org/papers/v23/21-0758.html)

- Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification [[TPAMI2023]]() - [[PyTorch]]()

- Bayesian modeling of uncertainty in low-level vision [[IJCV1990]]()

**Arxiv**

- Density Uncertainty Layers for Reliable Uncertainty Estimation [[arXiv2023]]()

## Ensemble-Methods

**Conference**

- Input-gradient space particle inference for neural network ensembles [[ICLR2024]]()

- Fast Ensembling with Diffusion Schrödinger Bridge [[ICLR2024]]()

- Pathologies of Predictive Diversity in Deep Ensembles [[ICLR2024]]()

- Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization [[ICML2023]]()

- Bayesian Posterior Approximation With Stochastic Ensembles [[CVPR2023]]()

- Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling [[AAAI2023]]()

- Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models [[ICCV2023]]() - [[PyTorch]]()

- Weighted Ensemble Self-Supervised Learning [[ICLR2023]]()

- Agree to Disagree: Diversity through Disagreement for Better Transferability [[ICLR2023]]() - [[PyTorch]]()

- Packed-Ensembles for Efficient Uncertainty Estimation [[ICLR2023]]() - [[TorchUncertainty]]()

- Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks [[ICCV Workshop2023]]()

- Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks [[AAAI2022]]()

- Deep Ensembles Work, But Are They Necessary? [[NeurIPS2022]]()

- FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation [[NeurIPS2022]]()

- Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity [[ICLR2022]]() - [[PyTorch]]()

- On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection [[ECCV Workshop2022]]()

- Masksembles for Uncertainty Estimation [[CVPR2021]]() - [[PyTorch/TensorFlow]]()

- Robustness via Cross-Domain Ensembles [[ICCV2021]]() - [[PyTorch]]()

- Uncertainty in Gradient Boosting via Ensembles [[ICLR2021]]() - [[PyTorch]]()

- Uncertainty Quantification and Deep Ensembles [[NeurIPS2021]]()

- Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles [[AAAI2020]]()

- Uncertainty in Neural Networks: Approximately Bayesian Ensembling [[AISTATS 2020]]()

- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [[ICLR2020]]() - [[PyTorch]]()

- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [[ICLR2020]]() - [[TensorFlow]]() - [[TorchUncertainty]]()

- Hyperparameter Ensembles for Robustness and Uncertainty Quantification [[NeurIPS2020]]()

- Bayesian Deep Ensembles via the Neural Tangent Kernel [[NeurIPS2020]]()

- Diversity with Cooperation: Ensemble Methods for Few-Shot Classification [[ICCV2019]]()

- Accurate Uncertainty Estimation and Decomposition in Ensemble Learning [[NeurIPS2019]]()

- High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach [[ICML2018]]() - [[TensorFlow]]()

- Snapshot Ensembles: Train 1, get M for free [[ICLR2017]](https://arxiv.org/abs/1704.00109) - [[TorchUncertainty]]()

- Simple and scalable predictive uncertainty estimation using deep ensembles [[NeurIPS2017]]() - [[TorchUncertainty]]()

**Journal**

- One Versus all for deep Neural Network for uncertainty (OVNNI) quantification [[IEEE Access2021]]()

**Arxiv**

- Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting [[arXiv2023]]()

- Deep Ensemble as a Gaussian Process Approximate Posterior [[arXiv2022]]()

- Sequential Bayesian Neural Subnetwork Ensembles [[arXiv2022]]()

- Confident Neural Network Regression with Bootstrapped Deep Ensembles [[arXiv2022]]() - [[TensorFlow]]()

- Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [[arXiv2021]]()

- Deep Ensembles: A Loss Landscape Perspective [[arXiv2019]]()

- Checkpoint ensembles: Ensemble methods from a single training process [[arXiv2017]]() - [[TorchUncertainty]]()

## Sampling/Dropout-based-Methods

**Conference**

- Enabling Uncertainty Estimation in Iterative Neural Networks [[ICML2024]]() - [[Pytorch]]()

- Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models [[CVPR2024]]() - [[TorchUncertainty]]()

- Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate [[AAAI2022]]()

- Efficient Bayesian Uncertainty Estimation for nnU-Net [[MICCAI2022]]()

- Dropout Sampling for Robust Object Detection in Open-Set Conditions [[ICRA2018]]()

- Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks [[MIDL2018]]()

- Concrete Dropout [[NeurIPS2017]]()

- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning [[ICML2016]]() - [[TorchUncertainty]]()

**Journal**

- A General Framework for Uncertainty Estimation in Deep Learning [[Robotics and Automation Letters2020]]()

**Arxiv**

- SoftDropConnect (SDC) – Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis [[arXiv2022]]()

## Post-hoc-Methods/Auxiliary-Networks

**Conference**

- On the Limitations of Temperature Scaling for Distributions with Overlaps [[ICLR2024]](https://arxiv.org/abs/2306.00740)

- Post-hoc Uncertainty Learning using a Dirichlet Meta-Model [[AAAI2023]]() - [[PyTorch]]()

- ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models [[ICCV2023]]()

- Out-of-Distribution Detection for Monocular Depth Estimation [[ICCV2023]]()

- Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model [[AAAI2022]]()

- Learning Structured Gaussians to Approximate Deep Ensembles [[CVPR2022]]()

- Improving the reliability for confidence estimation [[ECCV2022]]()

- Gradient-based Uncertainty for Monocular Depth Estimation [[ECCV2022]]() - [[PyTorch]]()

- BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks [[ECCV2022]]() - [[PyTorch]]()

- Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [[ICIP2022]]()

- SLURP: Side Learning Uncertainty for Regression Problems [[BMVC2021]]() - [[PyTorch]]()

- Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [[ICCV2021]]() - [[PyTorch]]()

- Learning to Predict Error for MRI Reconstruction [[MICCAI2021]]()

- A Mathematical Analysis of Learning Loss for Active Learning in Regression [[CVPR Workshop2021]]()

- Real-time uncertainty estimation in computer vision via uncertainty-aware distribution distillation [[WACV2021]]()

- On the uncertainty of self-supervised monocular depth estimation [[CVPR2020]]() - [[PyTorch]]()

- Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel [[ICLR2020]]() - [[TensorFlow]]()

- Gradients as a Measure of Uncertainty in Neural Networks [[ICIP2020]]()

- Learning Loss for Test-Time Augmentation [[NeurIPS2020]]()

- Learning loss for active learning [[CVPR2019]]() - [[PyTorch]]() (unofficial codes)

- Addressing failure prediction by learning model confidence [[NeurIPS2019]]() - [[PyTorch]]()

- Structured Uncertainty Prediction Networks [[CVPR2018]]() - [[TensorFlow]]()

- Classification uncertainty of deep neural networks based on gradient information [[IAPR Workshop2018]]()

**Journal**

- Towards More Reliable Confidence Estimation [[TPAMI2023]]()

- Confidence Estimation via Auxiliary Models [[TPAMI2021]]()

**Arxiv**

- Instance-Aware Observer Network for Out-of-Distribution Object Segmentation [[arXiv2022]]()

- DEUP: Direct Epistemic Uncertainty Prediction [[arXiv2020]]()

- Learning Confidence for Out-of-Distribution Detection in Neural Networks [[arXiv2018]]()

## Data-augmentation/Generation-based-methods

**Conference**

- Posterior Uncertainty Quantification in Neural Networks using Data Augmentation [[AISTATS2024]]()

- Learning to Generate Training Datasets for Robust Semantic Segmentation [[WACV2024]]()

- OpenMix: Exploring Outlier Samples for Misclassification Detection [[CVPR2023]]() - [[PyTorch]]()

- On the Pitfall of Mixup for Uncertainty Calibration [[CVPR2023]]()

- Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates [[AAAI2022]]()

- Out-of-distribution Detection with Implicit Outlier Transformation [[ICLR2023]]() - [[PyTorch]]()

- PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures [[CVPR2022]]()

- RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness [[NeurIPS2022]]() - [[PyTorch]]()

- Towards efficient feature sharing in MIMO architectures [[CVPR Workshop2022]]()

- Robust Semantic Segmentation with Superpixel-Mix [[BMVC2021]]() - [[PyTorch]]()

- MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks [[ICCV2021]]() - [[PyTorch]]()

- Training independent subnetworks for robust prediction [[ICLR2021]]()

- Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness [[IJCAI2021]]() - [[PyTorch]]()

- Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement  [[ICCV Workshop2021]]()

- Uncertainty-Aware Deep Classifiers using Generative Models [[AAAI2020]]()

- Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation [[ECCV2020]]() - [[PyTorch]]()

- Detecting the Unexpected via Image Resynthesis [[ICCV2019]]() - [[PyTorch]]()

- Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning [[ICML2020]]()

- Deep Anomaly Detection with Outlier Exposure [[ICLR2019]]()

- On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks [[NeurIPS2019]]()

**Arxiv**

- Reliability in Semantic Segmentation: Can We Use Synthetic Data? [[arXiv2023]]()

- Quantifying uncertainty with GAN-based priors [[arXiv2019]]() - [[TensorFlow]]()

## Output-Space-Modeling/Evidential-deep-learning

**Conference**

- Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty [[ICLR2024]](https://arxiv.org/abs/2404.10980)

- The Evidence Contraction Issue in Deep Evidential Regression: Discussion and Solution [[AAAI2024]]()

- Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression [[AAAI2024]]() - [[PyTorch]]()

- The Unreasonable Effectiveness of Deep Evidential Regression [[AAAI2023]]() - [[PyTorch]]() - [[TorchUncertainty]](https://github.com/ENSTA-U2IS-AI/torch-uncertainty)

- Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification [[CVPR2023]](https://arxiv.org/abs/2304.05165)

- Plausible Uncertainties for Human Pose Regression [[ICCV2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Bramlage_Plausible_Uncertainties_for_Human_Pose_Regression_ICCV_2023_paper.pdf) - [[PyTorch]]()

- Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [[ICML2023]](https://arxiv.org/pdf/2303.02045.pdf) - [[PyTorch]]()

- Improving Evidential Deep Learning via Multi-task Learning [[AAAI2022]]() - [[PyTorch]](https://github.com/deargen/MT-ENet)

- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers [[BELIEF2022]]()

- On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks [[ICLR2022]]() - [[PyTorch]](https://github.com/martius-lab/beta-nll)

- Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions [[ICLR2022]]() - [[PyTorch]]()

- Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation [[NeurIPS2022]]()

- Fast Predictive Uncertainty for Classification with Bayesian Deep Networks [[UAI2022]]() - [[PyTorch]]()

- Evaluating robustness of predictive uncertainty estimation: Are Dirichlet-based models reliable? [[ICML2021]]()

- Trustworthy multimodal regression with mixture of normal-inverse gamma distributions [[NeurIPS2021]]()

- Misclassification Risk and Uncertainty Quantification in Deep Classifiers [[WACV2021]]()

- Ensemble Distribution Distillation [[ICLR2020]]()

- Conservative Uncertainty Estimation By Fitting Prior Networks [[ICLR2020]]()

- Being Bayesian about Categorical Probability [[ICML2020]]() - [[PyTorch]]()

- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts  [[NeurIPS2020]]() - [[PyTorch]]()

- Deep Evidential Regression [[NeurIPS2020]]() - [[TensorFlow]]() - [[TorchUncertainty]]()

- Noise Contrastive Priors for Functional Uncertainty [[UAI2020]]()

- Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples [[NeurIPS Workshop2020]]()

- Uncertainty on Asynchronous Time Event Prediction [[NeurIPS2019]]() - [[TensorFlow]]()

- Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness [[NeurIPS2019]]()

- Quantifying Classification Uncertainty using Regularized Evidential Neural Networks [[AAAI FSS2019]]()

- Uncertainty estimates and multi-hypotheses networks for optical flow [[ECCV2018]](