mlsanityData to 7 Oct 2026

Generative Networks for Precision Enthusiasts

arXiv 2110.13632 ↗First posted 22 Oct 2021hep-ph cs.LGcited by 21

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Abstract (arXiv)

Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 33 of its 74 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

33 of 74 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. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  5. 2015Variational Inference with Normalizing Flowscited by 2,161
  6. 2016Density estimation using Real NVPcited by 2,029
  7. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  8. 2016Variational Inference: A Review for Statisticianscited by 1,466
  9. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  10. 2019An Introduction to Variational Autoencoderscited by 997
  11. 2017Generative Adversarial Networks: An Overviewcited by 588
  12. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 239
21 more
  1. 2018Neural Importance Samplingcited by 154
  2. 2019Guided Image Generation with Conditional Invertible Neural Networkscited by 147
  3. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  4. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  5. 2017Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesiscited by 46
  6. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  7. 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
  8. 2019Cubic-Spline Flowscited by 34
  9. 2019How to GAN away Detector Effectscited by 28
  10. 2020GANplifying Event Samplescited by 28
  11. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  12. 2020i-flow: High-dimensional Integration and Sampling with Normalizing Flowscited by 26
  13. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
  14. 2020Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)cited by 20
  15. 2018JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physicscited by 18
  16. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  17. 2020Towards a Computer Vision Particle Flowcited by 15
  18. 2020Variational Autoencoders for Jet Simulationcited by 14
  19. 2018Neural Network-Based Approach to Phase Space Integrationcited by 10
  20. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  21. 2020Improved Neural Network Monte Carlo Simulationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 8; 2025: 1; 2026: 1.

21 papers.

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  1. 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
  2. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  3. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  4. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  5. 2024The Landscape of Unfolding with Machine Learningcited by 20
  6. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  7. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
  8. 2022CaloFlow for CaloChallenge Dataset 1cited by 14
  9. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  10. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  11. 2023Calorimeter shower superresolutioncited by 7
  12. 2023Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Informationcited by 7
9 more
  1. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  2. 2024PIPPIN: Generating variable length full events from partonscited by 6
  3. 2024Generative Unfolding with Distribution Mappingcited by 6
  4. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  5. 2023ELSA -- Enhanced latent spaces for improved collider simulationscited by 5
  6. 2023Generative Invertible Quantum Neural Networkscited by 3
  7. 2025Forecasting Generative Amplificationcited by 3
  8. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  9. 2026Generative Models and Statistical Validationcited by 2