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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/luo-lorry/cti

공식 코드

Conformal Thresholded Intervals for Efficient Regression

https://github.com/m-schroder/continuouscausalcp

공식 코드

Conformal Prediction for Causal Effects of Continuous Treatments

https://github.com/macedaell/vanbayes

공식 코드

A variational neural Bayes framework for inference on intractable posterior distributions

https://github.com/mad-stat/EGDL

공식 코드

Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak

https://github.com/mad-stat/parnn

공식 코드

Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting

https://github.com/maltamiranomontero/RCGP

공식 코드

Robust and Conjugate Gaussian Process Regression

https://github.com/mamba413/cope

공식 코드

Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process

https://github.com/manuelhaussmann/bedl

공식 코드

Bayesian Evidential Deep Learning with PAC Regularization

https://github.com/maohaos2/PosthocUQ

공식 코드

Post-hoc Uncertainty Learning using a Dirichlet Meta-Model

https://github.com/maohaos2/edl-mirage

공식 코드

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?

https://github.com/maoxiaowei97/dutytte

공식 코드

DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation

https://github.com/markusheinonen/supervised-mollification

공식 코드

Robust Classification by Coupling Data Mollification with Label Smoothing

https://github.com/martinferianc/ComBiNet

공식 코드

ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

https://github.com/matsengrp/historydag

공식 코드

Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph

https://github.com/matteogaspa/coma

공식 코드

Conformal online model aggregation

https://github.com/matthewyccheung/conformal-metric

공식 코드

Regression Conformal Prediction under Bias

https://github.com/matthieudarcy/kernelsoperatorlearning

공식 코드

Kernel Methods are Competitive for Operator Learning

https://github.com/matthjs/xai-gp

공식 코드

Evaluating Uncertainty in Deep Gaussian Processes

https://github.com/mattwfranchi/street-flooding

공식 코드

Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection

https://github.com/mbonasnd/2025cesar

공식 코드

CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting

https://github.com/medical-image-analysis-laboratory/ms_wml_uncs

공식 코드

Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation

https://github.com/mengdanzhu/latentexplainer

공식 코드

LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models

https://github.com/meta-inf/fil

공식 코드

Fast Instrument Learning with Faster Rates

https://github.com/meta-inf/ipb

공식 코드

On Uncertainty Quantification for Near-Bayes Optimal Algorithms

https://github.com/meta-inf/qbdiv

공식 코드

Quasi-Bayesian Dual Instrumental Variable Regression

https://github.com/meta-inf/suq-nlg

공식 코드

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

https://github.com/meteoswiss/ldcast

공식 코드

Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

https://github.com/mfrdixon/Deep_Fundamental_Factors

공식 코드

Deep Fundamental Factor Models

https://github.com/mgoplerud/pfvi_glmm_replication

공식 코드

Partially factorized variational inference for high-dimensional mixed models

https://github.com/mgoplerud/vglmer

공식 코드

Partially factorized variational inference for high-dimensional mixed models

https://github.com/michaelplanknz/structured-inference

공식 코드

Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences

https://github.com/michaelzhang01/gpmoe

공식 코드

Sequential Gaussian Processes for Online Learning of Nonstationary Functions

https://github.com/mide478/lowerdimensionalspace-stabilization-rt-uqi

공식 코드

Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation

https://github.com/miilab-mtu/crt_multistageml_uncertainty

공식 코드

A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification

https://github.com/mingdeyu/DGP

공식 코드

Deep Gaussian Process Emulation using Stochastic Imputation

https://github.com/mir-group/nequip

공식 코드

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

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