mlsanityData to 7 Oct 2026

Understanding Event-Generation Networks via Uncertainties

arXiv 2104.04543 ↗First posted 9 Apr 2021hep-ph cs.LGcited by 17

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Abstract (arXiv)

Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight. Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 33 of its 83 references that are in the map. First posted on arXiv 9 Apr 2021.

Builds on

33 of 83 references.

References · 33

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  4. 2015Variational Inference with Normalizing Flowscited by 2,161
  5. 2016Density estimation using Real NVPcited by 2,029
  6. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  7. 2016Variational Inference: A Review for Statisticianscited by 1,466
  8. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  9. 2019An Introduction to Variational Autoencoderscited by 997
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21 more
  1. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 239
  2. 2018Neural Importance Samplingcited by 154
  3. 2020BayesFlow: Learning complex stochastic models with invertible neural networkscited by 139
  4. 2020Flows for simultaneous manifold learning and density estimationcited by 117
  5. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  6. 2020Anomaly Detection with Density Estimationcited by 71
  7. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
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  12. 2020GANplifying Event Samplescited by 28
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  14. 2020i-flow: High-dimensional Integration and Sampling with Normalizing Flowscited by 26
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  17. 2020Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkcited by 18
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 1; 2024: 6; 2025: 1; 2026: 1.

17 papers.

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  1. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  2. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  3. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  4. 2021Generative Networks for Precision Enthusiastscited by 21
  5. 2024The Landscape of Unfolding with Machine Learningcited by 20
  6. 2022CaloFlow for CaloChallenge Dataset 1cited by 14
  7. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  8. 2021Optimising simulations for diphoton production at hadron colliders using amplitude neural networkscited by 9
  9. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  10. 2024Generative Unfolding with Distribution Mappingcited by 6
  11. 2024Calibrating Bayesian Generative Machine Learning for Bayesiamplificationcited by 5
  12. 2022Bias and Priors in Machine Learning Calibrations for High Energy Physicscited by 3
5 more
  1. 2025Forecasting Generative Amplificationcited by 3
  2. 2026Uncertainty in Physics and AI: Taxonomy, Quantification, and Validationcited by 3
  3. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  4. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  5. 2022Generative models uncertainty estimationcited by 1