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Uncertainty in Deep Learning Papers

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Uncertainty & Confidence in Deeplearning Papers

Basic

  • 1995-Cordella-A Method for Improving Classification Reliability of Multilayer Perceptrons paper
  • 1999-Platt-Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods paper
  • 2001-Zadrozny-Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers paper
  • 2005-Niculescu-Mizil-Predicting Good Probabilities With Supervised Learning paper
  • 2005-Niculescu-Mizil-Obtaining Calibrated Probabilities from Boosting paper
  • 2014-Naeini-Binary Classifier Calibration: Non-parametric approach paper
  • 2014-VanderPals-Frequentism and Bayesianism: A Python-driven Primer paper
  • 2015-Naeini-Obtaining Well Calibrated Probabilities Using Bayesian Binning paper

Bayesian

  • 2015-Gal-Dropout as a Bayesian approximation paper
  • 2016-Gal-Dropout as a Bayesian Approximation:Representing Model Uncertainty in Deep Learning paper
  • 2016-McClure-Representing inferential uncertainty in deep neural networks through sampling paper
  • 2017-Kendall-What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? paper
  • 2018-Ayhan-Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks paper
  • 2018-Hafner-Noise Contrastive Priors for Functional Uncertainty paper
  • 2018-Sensoy-Evidential deep learning to quantify classification uncertainty paper
  • 2019-Seo-Learning for single-shot confidence calibration in deep neural networks through stochastic inferences paper
  • 2019-Ghandeharioun-Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias paper
  • 2019-Ahn-Uncertainty-based Continual Learning with Adaptive Regularization paper
  • 2020-Joo-Being Bayesian about Categorical Probability paper
  • 2020-Kristiadi-Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks paper

Uncertainty & Confidence

  • 2017-Kahn-Uncertainty-Aware Reinforcement Learning for Collision Avoidance paper
  • 2017-Lakshminarayanan-Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles paper
  • 2017-Guo-On Calibration of Modern Neural Networks paper
  • 2017-Mandelbaum-Distance-based Confidence Score for Neural Network Classifiers paper
  • 2017-Pereyra-Regularizing neural networks by penalizing confident output distributions paper
  • 2017-Pleiss-On Fairness and Calibration paper
  • 2017-Geifman-Selective Classification for Deep Neural Networks paper
  • 2018-Choi-Uncertainty-Aware Learning from Demonstration using Mixture Density Networks paper
  • 2018-Cortes-A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks paper
  • 2018-Jiang-To trust or not to trust a classifier paper
  • 2018-Gurau-Dropout Distillation for Efficiently Estimating Model Confidence paper
  • 2018-Kuleshob-Accurate Uncertainties for Deep Learning Using Calibrated Regression paper
  • 2018-Kumar-Trainable calibration measures for neural networks from kernel mean embeddings paper
  • 2018-Liu-Generalized zero-shot learning with deep calibration network paper
  • 2018-Mozafari-Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks paper
  • 2018-Neumann-Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection paper
  • 2018-Geifman-Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers paper
  • 2019-Hein-Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem paper
  • 2019-Bullock-XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets paper
  • 2019-Hendrycks-AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty paper
  • 2019-Perterson-Human uncertainty makes classification more robust paper
  • 2019-Neverova-Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels paper
  • 2019-Hendrycks-Using pre-training can improve model robustness and uncertainty paper
  • 2019-Ji-Bin-wise Temperature Scaling:Improvement in Confidence Calibration Performance through Simple Scaling Techniques paper
  • 2019-Shrikumar-Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks paper
  • 2019-Zhilu-Confidence Calibration for Convolutional Neural Networks Using Structured Dropout paper
  • 2019-Corbiere-Addressing Failure Prediction by learning model confidence paper
  • 2019-Kumar-Verified Uncertainty Calibration paper
  • 2019-Thulasidasan-On Mixup Training: Improved Calibration and Predictive Uncertainty for DNN paper
  • 2020-J van Amersfoort-Uncertainty Estimation Using a Single Deep Deterministic Neural Network paper
  • 2020-Moon-Confidence-Aware Learning for Deep Neural Networks paper
  • 2020-Corbiere-Confidence Estimation via Auxiliary Models paper
  • 2020-Charpentier-Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts paper
  • 2020-Wenzel-Hyperparameter Ensembles for Robustness and Uncertainty Quantification paper
  • 2020-S Desai-Calibration of Pre-trained Transformers paper
  • 2020-M Abdar-A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges paper
  • 2021-RJN Baldock-Deep Learning Through the Lens of Example Difficulty paper
  • 2021-M Minderer-Revisiting the Calibration of Modern Neural Networks paper
  • 2021-J van Amersfoort-On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty paper
  • 2021-J Mukhoti-Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty paper
  • 2021-C Tomani-Post-hoc Uncertainty Calibration for Domain Drift Scenarios paper
  • 2021-R Rahaman-Uncertainty Quantification and Deep Ensembles paper
  • 2021-I Galil-Disrupting Deep Uncertainty Estimation Without Harming Accuracy paper
  • 2021-DB Wang-Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence paper
  • paper

Out of Distribution Dectection

  • 2016-Hendrycks-A baseline for detecting misclassified and out-of-distribution examples in neural networks paper
  • 2017-Lee-Training confidence-calibrated classifiers for detecting out-of-distribution samples paper
  • 2017-Liang-Enhancing the reliability of out-of-distribution image detection in neural networks paper
  • 2018-DeVries-Learning Confidence for Out-of-Distribution Detection in Neural Networks paper
  • 2018-Hendrycks-Deep anomaly detection with outlier exposure paper
  • 2018-Li-Reducing Over-confident Errors outside the Known Distribution paper
  • 2019-Yu-Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy paper
  • 2019-Rohekar-Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections paper
  • 2019-Tagasovska-Single-Model Uncertainties for Deep Learning paper
  • 2019-Roady-Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets? paper
  • 2019-Sastry-Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices paper
  • 2019-AA Papadopoulos-Outlier Exposure with Confidence Control for Out-of-Distribution Detection paper
  • 2019-L Ruff-Deep Semi-Supervised Anomaly Detection paper
  • 2020-SerrĂ -Input complexity and out-of-distribution detection with likelihood-based generative models paper
  • 2020-J Tack-CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances paper
  • 2020-L Ruff-A Unifying Review of Deep and Shallow Anomaly Detection paper
  • 2020-D Hendrycks-The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization paper
  • 2020-W Liu-Energy-based Out-of-distribution Detection paper
  • 2020-YC Hsu-Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data paper
  • 2021-M Arjovsky -Out of Distribution Generalization in Machine Learning paper
  • 2021-D Zhang -Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? paper
  • 2021-X Wang-Undefined class-label detection vs out-of-distribution detection paper
  • 2021-C Berger-Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysispaper
  • 2021-S Fort-Exploring the Limits of Out-of-Distribution Detection paper
  • 2021-Z Lin-MOOD: Multi-level Out-of-distribution Detection paper
  • 2021-R Huang-MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space paper
  • 2021-A Zaeemzadeh-Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces paper
  • 2021-Y Sun-ReAct: Out-of-distribution Detection With Rectified Activations paper
  • 2021-K Bibas-Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection paper
  • 2021-M Zhang -On the Out-of-distribution Generalization of Probabilistic Image Modelling paper
  • paper

Adversarial Attack

  • 2019-Andrew Ilyas-Adversarial Examples Are Not Bugs, They Are Features paper
  • 2020-Finlay-Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks paper
  • 2020-H Salman-Do Adversarially Robust ImageNet Models Transfer Better? paper
  • 2020-J Zhang-Geometry-aware Instance-reweighted Adversarial Training paper
  • paper

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Uncertainty in Deep Learning Papers


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