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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/uncertainty-toolbox/uncertainty-toolbox

공식 코드

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

https://github.com/ur-whitelab/mol.dev

공식 코드

Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks

https://github.com/usccolumbia/materialsuq

공식 코드

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/ustclzh/pigpi

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Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations

https://github.com/uzh-dqbm-cmi/eustar_dgm4h

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Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes

https://github.com/uzh-dqbm-cmi/eustar_mlhc

공식 코드

Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

https://github.com/vaishwarya96/GroVE

공식 코드

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/discriminative-jackknife

공식 코드

Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/r2p-hte

공식 코드

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/rnn-blockwise-jackknife

공식 코드

Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

https://github.com/vanderschaarlab/sscp

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Improving Adaptive Conformal Prediction Using Self-Supervised Learning

https://github.com/vatass/adaptiveshrinkagedkgp

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Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories

https://github.com/versestech/cavi-cmn

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Gradient-free variational learning with conditional mixture networks

https://github.com/viluiz/gan

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Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification

https://github.com/vincent-1125/uncertainty-quantification-on-clinical-trial-outcome-prediction

공식 코드

Uncertainty Quantification on Clinical Trial Outcome Prediction

https://github.com/vincent-leguen/COMBO

공식 코드

Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction

https://github.com/vincentblot28/conformalized_gp

공식 코드

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees

https://github.com/vnemani14/uq_ml_review

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Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

https://github.com/wang-ml-lab/variational-imbalanced-regression

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Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing

https://github.com/wangsssky/MedicalMatting

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Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

https://github.com/wchen459/gan-duf

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GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

https://github.com/wearepal/qbold-vi

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Flexible Amortized Variational Inference in qBOLD MRI

https://github.com/weichangyu10/gpdapublic

공식 코드

Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data

https://github.com/weiyadi/vifo

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Variational Inference on the Final-Layer Output of Neural Networks

https://github.com/wenqian-ye/evidential_alignment

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Improving Group Robustness on Spurious Correlation via Evidential Alignment

https://github.com/wernerth94/bayesian-active-learning-by-distribution-disagreement

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Bayesian Active Learning By Distribution Disagreement

https://github.com/willembonnaffe/nodebngm

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Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data

https://github.com/williamlaplante/st-rcgp

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Robust and Conjugate Spatio-Temporal Gaussian Processes

https://github.com/wiseodd/bayesian_ood_training

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Being a Bit Frequentist Improves Bayesian Neural Networks

https://github.com/wjmaddox/swa_gaussian

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A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/wmdataphys/bayesian-hr-kan

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Uncertainty Quantification with Bayesian Higher Order ReLU KANs

https://github.com/wonbinkweon/unc_llm_rec_www2025

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Uncertainty Quantification and Decomposition for LLM-based Recommendation

https://github.com/wulongfeng/calirare

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Towards Reliable Rare Category Analysis on Graphs via Individual Calibration

https://github.com/wwu-mmll/uncertainty-brain-age

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An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

https://github.com/xiangyan93/molecules-enumerate

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Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation

https://github.com/xiaowu0162/sync-ralm-faithfulness

공식 코드

Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation

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