Papers with Code 아카이브

Uncertainty Quantification

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코드

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

https://bitbucket.org/gramacylab/deepgp-ex

공식 코드

Active Learning for Deep Gaussian Process Surrogates

https://bitbucket.org/jhhuggins/lrcoresets

공식 코드

Coresets for Scalable Bayesian Logistic Regression

https://bitbucket.org/jingweisong/uncertainty-estimation-for-hyper-spectral-image-denoising

공식 코드

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

https://git.skewed.de/count0/graph-tool

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Uncertainty quantification and posterior sampling for network reconstruction

https://github.com/1995subhankar1995/ncp

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Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis

https://github.com/2454511550lin/tamefalsepositives-ood

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Taming False Positives in Out-of-Distribution Detection with Human Feedback

https://github.com/4flixt/2022_paper_bll_lml

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Improved uncertainty quantification for neural networks with Bayesian last layer

https://github.com/4flixt/2023_paper_bll_lml

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Improved uncertainty quantification for neural networks with Bayesian last layer

https://github.com/7201krap/csce679_data-visualization

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Visualizing Uncertainty in Translation Tasks: An Evaluation of LLM Performance and Confidence Metrics

https://github.com/AI2ES/miles-guess

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Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

https://github.com/AMICI-dev/AMICI

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

https://github.com/ATOMconsortium/AMPL

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AMPL: A Data-Driven Modeling Pipeline for Drug Discovery

https://github.com/AaltoML/BayesVLM

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Post-hoc Probabilistic Vision-Language Models

https://github.com/AaltoML/sfr-experiments

공식 코드

Function-space Parameterization of Neural Networks for Sequential Learning

https://github.com/AdamCobb/hamiltorch

공식 코드

Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting

https://github.com/AdityaLab/EpiFNP

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When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

https://github.com/AlexImmer/Laplace

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Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/AnaryaRay1/naz

공식 코드

Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows

https://github.com/Annie983284450-1/CPGapBandit

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Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal Prediction

https://github.com/Annie983284450-1/NeuralLCB_C

공식 코드

NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction

https://github.com/AnonymousSAUC/SAUC

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SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

https://github.com/ArtemVazh/token_mahalanobis_distance

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

https://github.com/ArthurHoa/deep-eknn

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Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition

https://github.com/BU-DEPEND-Lab/SCUQ-RRG

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Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation

https://github.com/BayesRays/BayesRays

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Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

https://github.com/BayesWatch/deep-kernel-transfer

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Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

https://github.com/CFD-UTSA/WRF-bLES-PyPlume

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Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)

https://github.com/CS-SAIL/UQ-GNN-ROC

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Conditional Prediction ROC Bands for Graph Classification

https://github.com/CV-IP/uqvi

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Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

https://github.com/CardiacModelling/fickleheart-method-tutorials

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Considering discrepancy when calibrating a mechanistic electrophysiology model

https://github.com/ChuanqiChenCC/CGKN

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CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation

https://github.com/DILiS-lab/drivers-of-predictive-aleatoric-uncertainty

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Identifying Drivers of Predictive Aleatoric Uncertainty

https://github.com/Deep-Machine-Vision/he-cka-ensembles

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Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA

https://github.com/ENSTA-U2IS-AI/Label-smoothing-Selective-classification-Code

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Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

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

https://github.com/EmanuelSommer/MILE

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Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks

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