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Uncertainty Quantification

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MIC-DKFZ/TractSeg

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/MS-Mind/MS-Code-08/tree/main/NFNet

Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/Mind23-2/MindCode-101/tree/main/lenet

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

https://github.com/Mind23-2/MindCode-152

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/NajibYavari/DD2412

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/PatrickTUM/UnCRtainTS

UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series

https://github.com/RIVeR-Lab/Stochastic-MPPI

EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

https://github.com/Rose-STL-Lab/Spatiotemporal_UQ

Quantifying Uncertainty in Deep Spatiotemporal Forecasting

https://github.com/Ruhallah93/superior-scoring-rules

Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Tasks

https://github.com/SamuelGuilluy/Bayesian_ML_SWAG

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation

Unsupervised Quality Estimation for Neural Machine Translation

https://github.com/StanfordASL/SCOD

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/TeaPearce/Deep_Learning_Prediction_Intervals

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

https://github.com/VLL-HD/IB-INN

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

https://github.com/VLL-HD/exact_information_bottleneck

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

https://github.com/VirgiAgl/STVB

Structured Variational Inference in Continuous Cox Process Models

https://github.com/YeongHyeon/SWA-Gaussian-TF2

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/ZongrenZou/MHPINN

L-HYDRA: Multi-Head Physics-Informed Neural Networks

https://github.com/adamzenith/mapie

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/agnesdeng/misle

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/ahmedemam576/confident_explanations/blob/main/README.md

Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness

https://github.com/aigen/df-posthoc-calibration

Distribution-free binary classification: prediction sets, confidence intervals and calibration

https://github.com/alaalab/webcp

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

https://github.com/alexlimh/DPR_MUF

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

https://github.com/andreasgrv/emojivote

Deep Deterministic Uncertainty: A Simple Baseline

https://github.com/anfelopera/lineqGPR

Finite-dimensional Gaussian approximation with linear inequality constraints

https://github.com/anonymousest/DBSN

Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure

https://github.com/aredier/monte_carlo_dropout

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/arnavc1712/DeepEnsembles

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/arneschmidt/bayesian_deep_learning

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/arodriguezca/uncertainty-ts-forecasting

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/asharakeh/probdet

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/asonabend/dtr_via_surrogate_loss

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

https://github.com/atilberk/evidential-deep-learning-to-quantify-classification-uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/aurelio-amerio/concretedropout

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.