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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/bulbulahmmed/ML-to-reactive-mixing-data

공식 코드

A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing

https://github.com/bvanbreugel/deep_generative_ensemble

공식 코드

Synthetic data, real errors: how (not) to publish and use synthetic data

https://github.com/caiqizh/LUQ

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LUQ: Long-text Uncertainty Quantification for LLMs

https://github.com/cambridge-mlg/sampled-laplace

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Sampling-based inference for large linear models, with application to linearised Laplace

https://github.com/cambridgecia/cytodiffusion

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Deep Generative Classification of Blood Cell Morphology

https://github.com/cars-lab-repo/noodle

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Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learning

https://github.com/cbg-ethz/bmi

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On the Properties and Estimation of Pointwise Mutual Information Profiles

https://github.com/cbuelt/dduq

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Uncertainty quantification for data-driven weather models

https://github.com/cbuelt/pfno

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Probabilistic neural operators for functional uncertainty quantification

https://github.com/cbuelt/spatio-temporal-extremes

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Estimation of spatio-temporal extremes via generative neural networks

https://github.com/cdslaborg/paramonte

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Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library

https://github.com/cellistigs/interp_ensembles

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Deep Ensembles Work, But Are They Necessary?

https://github.com/charlesm93/vi-ordering

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Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

https://github.com/chenruduan/oareactdiff

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Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

https://github.com/chenzhao2023/multiomicsuncertainty

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SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data

https://github.com/chikap421/mseg_vcuq

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MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data

https://github.com/christian-helms/mpbnns

공식 코드

Approximate Message Passing for Bayesian Neural Networks

https://github.com/chunfengc/ASNet

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Active Subspace of Neural Networks: Structural Analysis and Universal Attacks

https://github.com/cics-nd/ar-pde-cnn

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Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks

https://github.com/cics-nd/dcedn-gcs

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Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

https://github.com/cics-nd/pde-surrogate

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Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

https://github.com/cics-nd/rans-uncertainty

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Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

https://github.com/clott3/multi-sym-ensem

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Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries

https://github.com/clu5/federated-conformal

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Federated Conformal Predictors for Distributed Uncertainty Quantification

https://github.com/clu5/lumbar-conformal

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Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets

https://github.com/cmpatino/utility_driven_prediction

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Decision-Focused Uncertainty Quantification

https://github.com/cognizant-ai-labs/semantic-density-paper

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Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space

https://github.com/comeusr/low-precisionsghmc

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Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo

https://github.com/comp-imaging-sci/mining-tomo-solutions-pulse

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Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems

https://github.com/congriUQ/physics_aware_surrogate

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A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

https://github.com/connor-duffin/ula-statfem

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Statistical Finite Elements via Langevin Dynamics

https://github.com/cortys/gpn-extensions

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Linear Opinion Pooling for Uncertainty Quantification on Graphs

https://github.com/creator-xi/Error-quantified-Conformal-Inference

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Error-quantified Conformal Inference for Time Series

https://github.com/csccm-iitd/glunet

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Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification

https://github.com/cu-uq/bi-fidelity-vae

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Bi-fidelity Variational Auto-encoder for Uncertainty Quantification

https://github.com/cviviers/prob_3d_segmentation

공식 코드

Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data

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