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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/tachella/equivariant_bootstrap

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Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems

https://github.com/tailabtmu/uq_gnn

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GNN's Uncertainty Quantification using Self-Distillation

https://github.com/takuomatsubara/WGBoost

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Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning

https://github.com/tang39/DLADA

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A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage

https://github.com/tanguybosser/conf_tpp

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Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes

https://github.com/tarolangner/ukb_mimir

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Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI

https://github.com/tbortolotti/marginal-noise-adaptive-conformal

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Noise-Adaptive Conformal Classification with Marginal Coverage

https://github.com/team-daniel/mc-cp

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Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction

https://github.com/testingautomated-usi/corrupted-text

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Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)

https://github.com/testingautomated-usi/dnn-tip

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Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)

https://github.com/testingautomated-usi/simple-tip

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Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)

https://github.com/testingautomated-usi/uncertainty-wizard

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Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

https://github.com/thanostriantafyllou3/ph-dropout

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PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis

https://github.com/themisbo/Rule-based-Bayesian-regr

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Rule-based Bayesian regression

https://github.com/themisbo/rule-based-evol-bayesian-learn

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Rule-based Evolutionary Bayesian Learning

https://github.com/thgaskin/neuralabm

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Inferring networks from time series: a neural approach

https://github.com/thinkxca/RUL

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Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life

https://github.com/thudzj/ScalableBDL

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LiBRe: A Practical Bayesian Approach to Adversarial Detection

https://github.com/timrensmeyer/Convergence-Experiments

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On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors

https://github.com/tingyuansen/statml

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Statistical Machine Learning for Astronomy -- A Textbook

https://github.com/tisabe/jraph_mpeu

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Band-gap regression with architecture-optimized message-passing neural networks

https://github.com/tjvandal/discrete-continuous-bdl

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Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning

https://github.com/tmanole/SW-inference

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Minimax Confidence Intervals for the Sliced Wasserstein Distance

https://github.com/tobiasriedlinger/gradient-metrics-od

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Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

https://github.com/tonyckc/uda-bua

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Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment

https://github.com/tonyzyl/ladcast

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LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting

https://github.com/tonyzyl/semisupervised-vae-for-regression-application-on-soft-sensor

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Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors

https://github.com/tpouplin/rqr

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Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise

https://github.com/trevorcampbell/bayesian-coresets

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Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

https://github.com/tum-pbs/diffusion-based-flow-prediction

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Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models

https://github.com/tummfm/jax-sgmc

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JaxSGMC: Modular stochastic gradient MCMC in JAX

https://github.com/twitter-research/double-corrected-variance-estimator

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De-biasing "bias" measurement

https://github.com/ucsb-nlp-chang/llm_uncertainty

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Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling

https://github.com/ukgovernmentbeis/hibayes

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HiBayES: A Hierarchical Bayesian Modeling Framework for AI Evaluation Statistics

https://github.com/umdatasciencelab/uncertainty-quantification-for-llms

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Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models

https://github.com/umkiyoung/dibo

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Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization

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