mlsanityData to 7 Oct 2026

Neural Embedding: Learning the Embedding of the Manifold of Physics Data

arXiv 2208.05484 ↗First posted 10 Aug 2022hep-ph cs.LG hep-excited by 7

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Abstract (arXiv)

In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 74 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

27 of 74 references.

References · 27

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  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  5. 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 674
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  8. 2021Hyperbolic Deep Neural Networks: A Surveycited by 138
  9. 2020Anomaly Detection with Density Estimationcited by 71
  10. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 42
  11. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 40
  12. 2019Divide and Conquer the Embedding Space for Metric Learningcited by 39
15 more
  1. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  2. 2017Learning Wasserstein Embeddingscited by 25
  3. 2018Novelty Detection Meets Collider Physicscited by 20
  4. 2021Neural Distance Embeddings for Biological Sequencescited by 17
  5. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  6. 2019Learning Embeddings into Entropic Wasserstein Spacescited by 15
  7. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  8. 2018Learning Role-based Graph Embeddingscited by 14
  9. 2020Fast Sequence-Based Embedding with Diffusion Graphscited by 14
  10. 2021Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distancecited by 10
  11. 2021An Exploration of Learnt Representations of W Jetscited by 8
  12. 2022"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detectioncited by 7
  13. 2021New Methods and Datasets for Group Anomaly Detection From Fundamental Physicscited by 6
  14. 2016WordNet2Vec: Corpora Agnostic Word Vectorization Methodcited by 2
  15. 2015A Visual Embedding for the Unsupervised Extraction of Abstract Semanticscited by 1

Built on by

Cited by year (2026 to 7 Oct)

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

7 papers.

  1. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 41
  2. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  3. 2024Universal New Physics Latent Spacecited by 2
  4. 2024Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learningcited by 1
  5. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0
  6. 2026Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantificationcited by 0
  7. 2026Learning the Geometry of Collider Events with Metric-Aware Deep Setscited by 0