https://github.com/badlands-model/Bayeslands_Workshop
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
Papers with Code 아카이브
Uncertainty Quantification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/beamlab-hsph/coverage_quantification
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
https://github.com/blacKitten13/Dropout-for-Model-Uncertainty
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/bond005/yandex-shifts-weather
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
https://github.com/cambridge-mlg/acp
Uncertainty Sets for Image Classifiers using Conformal Prediction
https://github.com/cameronccohen/deep-ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/cdebeunne/uncertainties_CNN
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/codelion/adaptive-classifier
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
https://github.com/coperception/double-m-quantification
Uncertainty Quantification of Collaborative Detection for Self-Driving
https://github.com/cpark321/bayesian-neural-networks
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/cpark321/uncertainty-deep-learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/crunch-uq4mi/neuraluq
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
https://github.com/cxu-tri/fail-detect
https://github.com/danielmenno/greek-estimation
https://github.com/deebuls/deep_evidential_regression_loss_pytorch
https://github.com/deu439/MCMC-optical-flow
https://github.com/dhavala/bqr
Estimation and Applications of Quantiles in Deep Binary Classification
https://github.com/dmlc/dgl/tree/master/examples/pytorch/dimenet
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
https://github.com/dougbrion/pytorch-classification-uncertainty
Evidential Deep Learning to Quantify Classification Uncertainty
https://github.com/dougbrion/pytorch-deep-ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/dsgiitr/ML-Reproducibility-2020-DUQ
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch
https://github.com/ensta-u2is/torch-uncertainty
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
https://github.com/erickgalinkin/dropout_privacy
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/facebookresearch/slbo
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
https://github.com/flynnye/ur-ern
https://github.com/giannifranchi/LP_BNN
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
https://github.com/github-jnauta/pytorch-pne
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/gknilsen/pyhessian
Epistemic Uncertainty Quantification in Deep Learning Classification by the Delta Method
https://github.com/gpflow/geometrickernels
https://github.com/gtegner/hyper-gan
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/hamrel-cxu/EnbPI
https://github.com/hayoung-kim/tf2-deep-ensemble-uncertainty
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/helange23/from_koopman_to_fourier
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
https://github.com/herbps10/adaptiveconformal
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.