mlsanityData to 13 Oct 2026

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

arXiv 2111.12849 ↗First posted 24 Nov 2021physics.data-an cs.LG hep-excited by 10

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Abstract (arXiv)

Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at the CERN Large Hadron Collider. We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representations and can leverage the interdependencies among the particles within a jet, for such tasks. Additionally, we develop a differentiable approximation to the energy mover's distance via a graph neural network, which may subsequently be used as a reconstruction loss function for autoencoders.

Key facts. Cited by 10 papers in the map (data through 13 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 24 Nov 2021.

Builds on

18 of 43 references.

References · 18

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,991
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,422
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,295
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,222
  5. 2018Relational inductive biases, deep learning, and graph networkscited by 1,353
  6. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,147
  7. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 976
  8. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 505
  9. 2020Flows for simultaneous manifold learning and density estimationcited by 117
  10. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  11. 2019FSPool: Learning Set Representations with Featurewise Sort Poolingcited by 53
  12. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
6 more
  1. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 40
  2. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  3. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
  4. 2021Bump Hunting in Latent Spacecited by 19
  5. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  6. 2020Linearized Optimal Transport for Collider Eventscited by 16

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 0; 2025: 2; 2026: 1.

10 papers.

  1. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  2. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  3. 2022Lorentz group equivariant autoencoderscited by 15
  4. 2023Anomalies, Representations, and Self-Supervisioncited by 10
  5. 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 7
  6. 2022Machine-Learning Compression for Particle Physics Discoveriescited by 3
  7. 2023Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHCcited by 1
  8. 2025A Step Toward Interpretability: Smearing the Likelihoodcited by 1
  9. 2025Non-Gaussianities in Collider Metric Binningcited by 0
  10. 2026Learning the Geometry of Collider Events with Metric-Aware Deep Setscited by 0