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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/zankin/sovbr

공식 코드

Sparse online variational Bayesian regression

https://github.com/zbox1005/cot-uq

공식 코드

CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought

https://github.com/zhang-jinghua/dbc-fscil

공식 코드

A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill Recognition

https://github.com/zhaobinmo/trafficflowgan

공식 코드

TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification

https://github.com/zhiyuan-gg/conformal-uncertainty-criterion

공식 코드

ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

https://github.com/zhuangdingyi/stzinb

공식 코드

Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks

https://github.com/zichongli5/smurf-thp

공식 코드

SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process

https://github.com/ziquanliu/ICML2024-AT-UR

공식 코드

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks

https://github.com/zlin7/lvd

공식 코드

Locally Valid and Discriminative Prediction Intervals for Deep Learning Models

https://github.com/zlin7/uq-nlg

공식 코드

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

https://github.com/zongrenzou/hjpde4uqsciml

공식 코드

Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning

https://github.com/zshuai8/FedGMM_ICML2023

공식 코드

Personalized Federated Learning under Mixture of Distributions

https://gitlab.com/milidris/qcwasserteinproj

공식 코드

Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models

https://agit.ai/Polixir/OfflineRL

COMBO: Conservative Offline Model-Based Policy Optimization

https://github.com/2024-MindSpore-1/Code3/tree/main/Dimenet

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

https://github.com/ATOMScience-org/AMPL

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery

https://github.com/AllaVinner/PCPCA-a-little-look

Probabilistic Contrastive Principal Component Analysis

https://github.com/BlackHC/ddu_dirty_mnist

Deep Deterministic Uncertainty: A Simple Baseline

https://github.com/BruceBinBoxing/Deep_Learning_Weather_Forecasting

Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

https://github.com/BruceBinBoxing/WF

Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

https://github.com/BruceBinBoxing/Weather_Forecasting

Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

https://github.com/Cogito2012/DEAR

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/ENSTA-U2IS/MUAD-Dataset

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/EugeneNdiaye/smoothed_concomitant_lasso

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

https://github.com/Eungyu-Jin/Dropout_uncertainty

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

https://github.com/FaceOnLive/Realtime-Background-Changer-SDK-Android

PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

https://github.com/FernandoTakenaka-Unifei/PCO102

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

https://github.com/Future-House/ldp

GAUCHE: A Library for Gaussian Processes in Chemistry

https://github.com/Glutamat42/epistemic_ensemble_toy_dataset

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/ICB-DCM/AMICI

AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

https://github.com/IntelLabs/AVUC

Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

https://github.com/IntelLabs/bayesian-torch

Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes

https://github.com/JoonHyung-Park/DeepEnsemble

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/Junghwan-brian/SDE-Net

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/Kyushik/Predictive-Uncertainty-Estimation-using-Deep-Ensemble

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/LukasMosser/geogan

Stochastic reconstruction of an oolitic limestone by generative adversarial networks

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