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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/nirgreshler/bayesian-online-planning

공식 코드

A Bayesian Approach to Online Planning

https://github.com/nmucke/data-assimilation

공식 코드

The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification

https://github.com/nppackages/binsreg

공식 코드

Binscatter Regressions

https://github.com/nrel/mluq-prop

공식 코드

A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks

https://github.com/nvidia/modulus-sym

공식 코드

Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model

https://github.com/nvlabs/conformalkeypoint

공식 코드

Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation

https://github.com/oleksandr-balabanov/equivariant-posteriors

공식 코드

Uncertainty quantification in fine-tuned LLMs using LoRA ensembles

https://github.com/omegafragger/DDU

공식 코드

Deep Deterministic Uncertainty: A Simple Baseline

https://github.com/omerb01/puq

공식 코드

Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems

https://github.com/omwright/cov-prop-nn

공식 코드

An Analytic Solution to Covariance Propagation in Neural Networks

https://github.com/opannekoucke/pdenetgen

공식 코드

PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations

https://github.com/orchardlanl/dpfehm.jl

공식 코드

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

https://github.com/p-manivannan/uq-motor-imagery

공식 코드

Uncertainty Quantification for cross-subject Motor Imagery classification

https://github.com/paddlepaddle/paddlespatial

공식 코드

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

https://github.com/pasteurlabs/unreasonable_effective_der

공식 코드

The Unreasonable Effectiveness of Deep Evidential Regression

https://github.com/patrickfan/genai4uq

공식 코드

GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models

https://github.com/paul-buerkner/bayesian-sparse-pce

공식 코드

A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms

https://github.com/paulkogni/SG-RCPS

공식 코드

Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy

https://github.com/paulkogni/mr-recon-uq

공식 코드

Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction

https://github.com/pawel-czyz/labelshift

공식 코드

Bayesian Quantification with Black-Box Estimators

https://github.com/pessoap/mamba-probtsf

공식 코드

Mamba time series forecasting with uncertainty quantification

https://github.com/peterdsharpe/neuralfoil

공식 코드

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning

https://github.com/petergroth/kermut

공식 코드

Kermut: Composite kernel regression for protein variant effects

https://github.com/philippreiser/bayesian-surrogate-uncertainty-paper

공식 코드

Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

https://github.com/phonchi/cryo-forum

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Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis

https://github.com/pingala-institute/Bayes-medicaldataimputation

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Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation

https://github.com/pnnl/expert2

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Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science

https://github.com/poetrywanderer/CF-NeRF

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Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification

https://github.com/poiuohke/UDC-Net

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Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

https://github.com/ppernot/2023_adaptivity

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Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity

https://github.com/ppernot/2023_primer

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Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts

https://github.com/ppernot/2024_rce

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Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks

https://github.com/ppernot/2024_simref

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Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis

https://github.com/pradhan-a/gp-dnn

공식 코드

Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression

https://github.com/pranavmaneriker/graphconformal-code

공식 코드

Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs

https://github.com/predict-idlab/cct-cmc

공식 코드

Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects

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