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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/xiaoyuxin1002/uq-plm

공식 코드

Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis

https://github.com/xingyan-fml/emq

공식 코드

Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification

https://github.com/xingyan-fml/usnrt

공식 코드

Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning

https://github.com/xuangu-fang/bayotide

공식 코드

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

https://github.com/y0ast/deterministic-uncertainty-quantification

공식 코드

Uncertainty Estimation Using a Single Deep Deterministic Neural Network

https://github.com/yairomer/ppde

공식 코드

Uncertainty Visualization via Low-Dimensional Posterior Projections

https://github.com/yanasgh/rafs

공식 코드

Toward Robust Uncertainty Estimation with Random Activation Functions

https://github.com/yanasgh/rafs_code

공식 코드

Toward Robust Uncertainty Estimation with Random Activation Functions

https://github.com/yandex-research/shifts

공식 코드

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

https://github.com/yashpatel5400/fusioncp

공식 코드

Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation

https://github.com/yavaryeganeh/aif_meeting_eec

공식 코드

Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines

https://github.com/yeon-lab/dream

공식 코드

Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging

https://github.com/yifei-liu-stat/pass

공식 코드

Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification

https://github.com/yihang-gao/wgan_pinns

공식 코드

Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

https://github.com/yikwill/thor-mom6

공식 코드

Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning

https://github.com/yiliu1998/Multi-Source-Conformal

공식 코드

Multi-Source Conformal Inference Under Distribution Shift

https://github.com/yinchangchang/sepsislab

공식 코드

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing

https://github.com/ying531/jomi-paper

공식 코드

Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage

https://github.com/yinghao-li/muben

공식 코드

MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/yingkaisha/aies_d_23_0094

공식 코드

Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model

https://github.com/yjliu7/neural-adaptive-empirical-bayes

공식 코드

Implicit Generative Prior for Bayesian Neural Networks

https://github.com/yonatandn/Uncertainty-extraction-in-Model-Based-DL

공식 코드

Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter

https://github.com/yongquan-qu/slams

공식 코드

Deep Generative Data Assimilation in Multimodal Setting

https://github.com/yongsukyee/uncertain_blackholemass

공식 코드

Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction

https://github.com/yransun/difr3ct

공식 코드

DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays

https://github.com/yromano/fair_dummies

공식 코드

Achieving Equalized Odds by Resampling Sensitive Attributes

https://github.com/yuanjiesh/rc3p

공식 코드

Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration

https://github.com/yuanyunshuang/gevbev

공식 코드

Generating Evidential BEV Maps in Continuous Driving Space

https://github.com/yunangao/basofr

공식 코드

Bayesian adaptive and interpretable functional regression for exposure profiles

https://github.com/yunshihuang/pmcnet

공식 코드

Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling

https://github.com/yuxin217/causalppi

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Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets

https://github.com/yyzhangecnu/copp

공식 코드

Conformal Off-policy Prediction

https://github.com/yzshi5/OpFlow

공식 코드

Universal Functional Regression with Neural Operator Flows

https://github.com/z5041294/optimization-and-pruning-for-bnn

공식 코드

On the optimization and pruning for Bayesian deep learning

https://github.com/zabaras/bayesmultiscale

공식 코드

A Bayesian Multiscale Deep Learning Framework for Flows in Random Media

https://github.com/zakariasenousy/mcua-model

공식 코드

MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

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