mlsanityData to 7 Oct 2026

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

arXiv 2209.07559 ↗First posted 15 Sep 2022physics.comp-ph cs.AI hep-excited by 10

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Abstract (arXiv)

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 74 of its 180 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

74 of 180 references.

References · 74

  1. 2016Deep Variational Information Bottleneckcited by 915
  2. 2018On the Robustness of Interpretability Methodscited by 310
  3. 2019Gauge Equivariant Convolutional Networks and the Icosahedral CNNcited by 262
  4. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
  5. 2020Lorentz Group Equivariant Neural Network for Particle Physicscited by 120
  6. 2018Energy Flow Networks: Deep Sets for Particle Jetscited by 83
  7. 2020Sampling using SU(N) gauge equivariant flowscited by 83
  8. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  9. 2020Anomaly Detection with Density Estimationcited by 71
  10. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  11. 2016Learning to Pivot with Adversarial Networkscited by 55
  12. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
38 more
  1. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  2. 2020Anomaly Detection With Conditional Variational Autoencoderscited by 46
  3. 2020Lattice gauge equivariant convolutional neural networkscited by 45
  4. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 42
  5. 2020Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4MLcited by 42
  6. 2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
  7. 2015Jet-Images -- Deep Learning Editioncited by 41
  8. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 40
  9. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  10. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  11. 2016Parameterized Machine Learning for High-Energy Physicscited by 33
  12. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
  13. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  14. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  15. 2020Simulation Assisted Likelihood-free Anomaly Detectioncited by 27
  16. 2020Fast inference of Boosted Decision Trees in FPGAs for particle physicscited by 26
  17. 2020Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image datacited by 23
  18. 2020Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAscited by 23
  19. 2021Point Cloud Transformers applied to Collider Physicscited by 23
  20. 2022Symmetry Group Equivariant Architectures for Physicscited by 23
  21. 2017QCD-Aware Recursive Neural Networks for Jet Physicscited by 22
  22. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
  23. 2020Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networkscited by 22
  24. 2016Deep learning in color: towards automated quark/gluon jet discriminationcited by 21
  25. 2018Novelty Detection Meets Collider Physicscited by 20
  26. 2021A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHCcited by 20
  27. 2020Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkcited by 18
  28. 2021Generalization capabilities of translationally equivariant neural networkscited by 18
  29. 2021SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attentioncited by 18
  30. 2018INFERNO: Inference-Aware Neural Optimisationcited by 16
  31. 2019Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Datacited by 16
  32. 2020Jet Flavour Classification Using DeepJetcited by 16
  33. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  34. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
  35. 2020Explainable AI for ML jet taggers using expert variables and layerwise relevance propagationcited by 14
  36. 2022Applications of Machine Learning to Lattice Quantum Field Theorycited by 14
  37. 2020Simulation-Assisted Decorrelation for Resonant Anomaly Detectioncited by 13
  38. 2021A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatorycited by 13

50 most cited of 74 All 74 (JSON)

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 3; 2025: 4.

10 papers.

  1. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  2. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 6
  3. 2022FAIR AI Models in High Energy Physicscited by 4
  4. 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networkscited by 4
  5. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  6. 2024Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural networkcited by 2
  7. 2024Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHCcited by 1
  8. 2025A Step Toward Interpretability: Smearing the Likelihoodcited by 1
  9. 2025Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIFcited by 0
  10. 2025Shedding Light on Dark Matter at the LHC with Machine Learningcited by 0