Papers with Code 아카이브

Uncertainty Quantification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

코드

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

https://github.com/Shen-Lab/BAL

공식 코드

Bayesian active learning for optimization and uncertainty quantification in protein docking

https://github.com/Simula-COMPLEX/pure

공식 코드

Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving

https://github.com/TY-Cheng/torchvinecopulib

공식 코드

Vine Copulas as Differentiable Computational Graphs

https://github.com/Tankevin998/GP_dPHS

공식 코드

Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models

https://github.com/TeaPearce/Bayesian_NN_Ensembles

공식 코드

Uncertainty in Neural Networks: Approximately Bayesian Ensembling

https://github.com/ToyotaCRDL/SimplexTS

공식 코드

Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex

https://github.com/Unco3892/semf-paper

공식 코드

SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals

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

공식 코드

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

https://github.com/Vahid-Negahdari/Elastic-Full-Waveforrm-Inversion

공식 코드

Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification

https://github.com/VhaoYan/PDE-constrained-DKL

공식 코드

PDE-DKL: PDE-constrained deep kernel learning in high dimensionality

https://github.com/Victorletzelter/VoronoiWTA

공식 코드

Winner-takes-all learners are geometry-aware conditional density estimators

https://github.com/Xiaohu-Zheng/Deep-aPCE

공식 코드

Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification

https://github.com/YSale/label-uq

공식 코드

Label-wise Aleatoric and Epistemic Uncertainty Quantification

https://github.com/YZ-Vista/Data-driven-dynamics_ID-DMD

공식 코드

Inverse Design with Dynamic Mode Decomposition

https://github.com/YangYongJin/MAQA-Official-Repo

공식 코드

MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty

https://github.com/YanxunXu/MaterialLawCausal

공식 코드

Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation

https://github.com/Yinghao-Li/UQAC

공식 코드

Language Model Uncertainty Quantification with Attention Chain

https://github.com/Yinghao-Li/UncertaintyBenchmark

공식 코드

MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/YoungseogChung/calibrated-quantile-uq

공식 코드

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

https://github.com/Yusufguvenx/Adapting-GT2-FLS-for-Uncertainty-Quantification-A-Blueprint-Calibration-Strategy

공식 코드

Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy

https://github.com/aalto-quml/climode

공식 코드

ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs

https://github.com/aaltoml/calibrated-dnn

공식 코드

Fixing Overconfidence in Dynamic Neural Networks

https://github.com/aamini/evidential-deep-learning

공식 코드

Deep Evidential Regression

https://github.com/aangelopoulos/conformal-classification

공식 코드

Uncertainty Sets for Image Classifiers using Conformal Prediction

https://github.com/aangelopoulos/conformal-prediction

공식 코드

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

https://github.com/aangelopoulos/conformal-time-series

공식 코드

Conformal PID Control for Time Series Prediction

https://github.com/aangelopoulos/conformal_classification

공식 코드

Uncertainty Sets for Image Classifiers using Conformal Prediction

https://github.com/aangelopoulos/im2im-uq

공식 코드

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

https://github.com/aangelopoulos/private_prediction_sets

공식 코드

Private Prediction Sets

https://github.com/aangelopoulos/rcps

공식 코드

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/abdulmajid-murad/deep_probabilistic_forecast

공식 코드

Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

https://github.com/abenechehab/adapts

공식 코드

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

https://github.com/ablancha/gpsearch

공식 코드

Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification

https://github.com/adhjort/ConformalPrediction

공식 코드

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction

https://github.com/adityavadlamani/conformal-fairness

공식 코드

A Generic Framework for Conformal Fairness

https://github.com/afrakilic/btn-kernel-machines

공식 코드

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

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