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Interpretable Uncertainty Quantification in AI for HEP

https://doi.org/10.2172/1886020
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4612-0745-0openalex-snapshot
Bayesian Learning for Neural Networks
inferredcites →https://doi.org/10.1007/978-3-030-50146-4_41openalex-snapshot
Evaluation of Uncertainty Quantification in Deep Learning
inferredcites →https://doi.org/10.1007/jhep05%282019%29036openalex-snapshot
Variational autoencoders for new physics mining at the Large Hadron Collider
inferredcites →https://doi.org/10.1016/j.cpc.2008.01.036openalex-snapshot
A brief introduction to PYTHIA 8.1
inferredcites →https://doi.org/10.1016/j.inffus.2021.05.008openalex-snapshot
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
inferredcites →https://doi.org/10.1016/j.strusafe.2008.06.020openalex-snapshot
Aleatory or epistemic? Does it matter?
inferredcites →https://doi.org/10.1073/pnas.1912789117openalex-snapshot
The frontier of simulation-based inference
inferredcites →https://doi.org/10.1073/pnas.1915980117openalex-snapshot
Mining gold from implicit models to improve likelihood-free inference
inferredcites →https://doi.org/10.1080/01621459.2017.1307116openalex-snapshot
Distribution-Free Predictive Inference for Regression
inferredcites →https://doi.org/10.1088/2632-2153/ae1f05openalex-snapshot
Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)
inferredcites →https://doi.org/10.1093/mnras/staa2799openalex-snapshot
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
inferredcites →https://doi.org/10.1093/mnras/stac1790openalex-snapshot
Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z
inferredcites →https://doi.org/10.1093/mnras/stz1960openalex-snapshot
Fast likelihood-free cosmology with neural density estimators and active learning
inferredcites →https://doi.org/10.1093/ptep/ptaa104openalex-snapshot
Review of Particle Physics
inferredcites →https://doi.org/10.1103/physrevd.102.063504openalex-snapshot
Accurate emulator for the redshift-space power spectrum of dark matter halos and its application to galaxy power spectrum
inferredcites →https://doi.org/10.1103/physrevlett.125.122001openalex-snapshot
Robust Jet Classifiers through Distance Correlation
inferredcites →https://doi.org/10.1109/hipc.2018.00014openalex-snapshot
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks
inferredcites →https://doi.org/10.1109/icpr56361.2022.9956231openalex-snapshot
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
inferredcites →https://doi.org/10.1109/tkde.2012.179openalex-snapshot
Dealing with Uncertainty: A Survey of Theories and Practices
inferredcites →https://doi.org/10.1140/epjc/s10052-008-0798-9openalex-snapshot
Herwig++ physics and manual
inferredcites →https://doi.org/10.1140/epjc/s10052-022-10012-wopenalex-snapshot
A cautionary tale of decorrelating theory uncertainties
inferredcites →https://doi.org/10.1140/epjc/s10052-022-11066-6openalex-snapshot
Application of transfer learning to neutrino interaction classification
inferredcites →https://doi.org/10.1145/1102351.1102430openalex-snapshot
Predicting good probabilities with supervised learning
inferredcites →https://doi.org/10.1145/775107.775151openalex-snapshot
Transforming classifier scores into accurate multiclass probability estimates
inferredcites →https://doi.org/10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2openalex-snapshot
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
inferredcites →https://doi.org/10.1371/journal.pcbi.1003542openalex-snapshot
Ten Simple Rules for the Care and Feeding of Scientific Data
inferredcites →https://doi.org/10.1609/aaai.v29i1.9602openalex-snapshot
Obtaining Well Calibrated Probabilities Using Bayesian Binning
inferredcites →https://doi.org/10.21105/astro.2105.05859openalex-snapshot
A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos
inferredcites →https://doi.org/10.21105/astro.2112.08423openalex-snapshot
Differentiable Predictions for Large Scale Structure with SHAMNet
inferredcites →https://doi.org/10.2307/1269106openalex-snapshot
Goodness-of-Fit Tests Based on P-P Probability Plots
inferredcites →https://doi.org/10.3390/e23121608openalex-snapshot
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
inferredcites →https://doi.org/10.3390/electronics8080832openalex-snapshot
Machine Learning Interpretability: A Survey on Methods and Metrics
inferredcites →https://doi.org/10.48550/arxiv.0706.3188openalex-snapshot
A tutorial on conformal prediction
inferredcites →https://doi.org/10.48550/arxiv.1211.0358openalex-snapshot
Deep Gaussian Processes
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1505.05770openalex-snapshot
Variational Inference with Normalizing Flows
inferredcites →https://doi.org/10.48550/arxiv.1506.02142openalex-snapshot
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1706.04599openalex-snapshot
On Calibration of Modern Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1707.02476openalex-snapshot
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1802.10501openalex-snapshot
Predictive Uncertainty Estimation via Prior Networks
inferredcites →https://doi.org/10.48550/arxiv.1904.01685openalex-snapshot
Measuring Calibration in Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1907.13167openalex-snapshot
The Aemulus Project IV: Emulating Halo Bias
inferredcites →https://doi.org/10.48550/arxiv.2102.10473openalex-snapshot
Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
inferredcites →https://doi.org/10.48550/arxiv.2102.10809openalex-snapshot
Local Calibration: Metrics and Recalibration
inferredcites →https://doi.org/10.48550/arxiv.2107.03342openalex-snapshot
A Survey of Uncertainty in Deep Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/jhep03(2026)219openalex-snapshot
Stable and interpretable jet physics with IRC-safe equivariant feature extraction
inferred← citeshttps://doi.org/10.1007/s41781-026-00166-7openalex-snapshot
Uncertainty Quantification for a Cramér Generative Adversarial Network-Based Simulation of the Ring-Imaging Cherenkov Particle Detector Using Feature Densities
inferred← citeshttps://doi.org/10.1007/s42452-025-07743-2openalex-snapshot
A comprehensive review of computer vision for reservoir modelling and data assimilation
inferred← citeshttps://doi.org/10.1088/2632-2153/ad9136openalex-snapshot
Calibrating Bayesian generative machine learning for Bayesiamplification
inferred← citeshttps://doi.org/10.1088/2632-2153/adf7feopenalex-snapshot
Uncertainty quantification from ensemble variance scaling laws in deep neural networks
inferred← citeshttps://doi.org/10.1088/2632-2153/ae1f05openalex-snapshot
Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)
inferred← citeshttps://doi.org/10.1140/epjs/s11734-024-01306-zopenalex-snapshot
Foundations of automatic feature extraction at LHC–point clouds and graphs
inferred← citeshttps://doi.org/10.1145/3706598.3713406openalex-snapshot
Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships
inferred← citeshttps://doi.org/10.21105/astro.2210.01666openalex-snapshot
Neural Network Based Point Spread Function Deconvolution For Astronomical Applications
inferred← citeshttps://doi.org/10.2172/1986455openalex-snapshot
Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops
inferred← citeshttps://doi.org/10.53846/goediss-10094openalex-snapshot
Improving Monte Carlo simulations in high energy physics using machine learning techniques
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