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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/cvlab-epfl/iter_unc

공식 코드

Enabling Uncertainty Estimation in Iterative Neural Networks

https://github.com/cvs-health/uqlm

공식 코드

Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers

https://github.com/danieledurante/ESBM

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Extended Stochastic Block Models with Application to Criminal Networks

https://github.com/danielrobertnicoud/imv-gps

공식 코드

Intrinsic Gaussian Vector Fields on Manifolds

https://github.com/danielyxyang/llm-verbalized-uq

공식 코드

On Verbalized Confidence Scores for LLMs

https://github.com/davidrburt/lipschitz-driven-inference

공식 코드

Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association

https://github.com/davidwarne/Warne2019_GuideToPseudoMarginal

공식 코드

A practical guide to pseudo-marginal methods for computational inference in systems biology

https://github.com/ddolab/BO4IO

공식 코드

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification

https://github.com/ddv-lab/Verifiably_Robust_CP

공식 코드

Verifiably Robust Conformal Prediction

https://github.com/deel-ai-papers/consema

공식 코드

Conformal Prediction for Image Segmentation Using Morphological Prediction Sets

https://github.com/deep-spin/sparse-activations-cp

공식 코드

Sparse Activations as Conformal Predictors

https://github.com/deep-spin/uncertainties_mt_eval

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Disentangling Uncertainty in Machine Translation Evaluation

https://github.com/deepskies/DeeplyUncertain-Public

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Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

https://github.com/deepskies/deepuq-neurips-ws-2024

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DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods

https://github.com/deepskies/domainadaptivemveforlensmodeling

공식 코드

Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

https://github.com/delicious-ai/ddpn

공식 코드

Flexible Heteroscedastic Count Regression with Deep Double Poisson Networks

https://github.com/deltadahl/cytodiffusion

공식 코드

Deep Generative Classification of Blood Cell Morphology

https://github.com/dgrzech/ir-sgmcmc

공식 코드

Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo

https://github.com/dhyuan99/veckm_flow

공식 코드

Learning Normal Flow Directly From Event Neighborhoods

https://github.com/dipuk0506/uq

공식 코드

Uncertainty Aware Neural Network from Similarity and Sensitivity

https://github.com/divake/Conformal_Prediction_based_Sensor_Trustworthiness_Detection

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Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception

https://github.com/divelab/AIRS

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Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

https://github.com/dmilios/dirichletGPC

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Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

https://github.com/dmml-geneva/calibrated-posterior

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Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability

https://github.com/dongweiye/rom-with-geometry-informed-snapshots

공식 코드

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

https://github.com/donlapark/coverforest

공식 코드

coverforest: Conformal Predictions with Random Forest in Python

https://github.com/dralgroup/mlatom

공식 코드

Physics-informed active learning for accelerating quantum chemical simulations

https://github.com/drewprinster/jaws

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JAWS: Auditing Predictive Uncertainty Under Covariate Shift

https://github.com/drewtnguyen/risk-tradeoffs-experiments

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Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction

https://github.com/dsashid/BOP-DMD

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Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification

https://github.com/dskoda/quests

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Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory

https://github.com/dtak/ocbnn-public

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Incorporating Interpretable Output Constraints in Bayesian Neural Networks

https://github.com/dtanoglidis/bnn_lsbgs_icml

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Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks

https://github.com/dvoina13/Dynamic_SINDy

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Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems

https://github.com/dweprinz/CP-TSD

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Conformal time series decomposition with component-wise exchangeability

https://github.com/dwu402/pypei

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Likelihood-based estimation and prediction for a measles outbreak in Samoa

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