{"total":18,"rows":[{"id":"2102.02770","title":"A Living Review of Machine Learning for Particle Physics","created":"2021-02-02","cited_by_count":78,"area":null},{"id":"2401.11351","title":"A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance","created":"2024-01-21","cited_by_count":32,"area":null},{"id":"2110.13632","title":"Generative Networks for Precision Enthusiasts","created":"2021-10-22","cited_by_count":21,"area":null},{"id":"2309.05704","title":"CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation","created":"2023-09-11","cited_by_count":20,"area":null},{"id":"2104.04543","title":"Understanding Event-Generation Networks via Uncertainties","created":"2021-04-09","cited_by_count":17,"area":null},{"id":"2101.07263","title":"E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once","created":"2021-01-18","cited_by_count":15,"area":null},{"id":"2405.20407","title":"Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows","created":"2024-05-30","cited_by_count":13,"area":null},{"id":"2306.03933","title":"High-dimensional and Permutation Invariant Anomaly Detection","created":"2023-06-06","cited_by_count":12,"area":null},{"id":"2511.15969","title":"A Primer on Quantum Machine Learning","created":"2025-11-20","cited_by_count":12,"area":null},{"id":"2305.10500","title":"Learning Likelihood Ratios with Neural Network Classifiers","created":"2023-05-17","cited_by_count":11,"area":null},{"id":"2110.04600","title":"A Review of Physics-based Machine Learning in Civil Engineering","created":"2021-10-09","cited_by_count":9,"area":null},{"id":"2311.18672","title":"A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks","created":"2023-11-30","cited_by_count":6,"area":null},{"id":"2305.07696","title":"ELSA -- Enhanced latent spaces for improved collider simulations","created":"2023-05-12","cited_by_count":5,"area":null},{"id":"2102.06128","title":"Sequence-based Machine Learning Models in Jet Physics","created":"2021-02-09","cited_by_count":3,"area":null},{"id":"2105.14933","title":"The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC","created":"2021-05-31","cited_by_count":3,"area":null},{"id":"2412.12074","title":"Extrapolating Jet Radiation with Autoregressive Transformers","created":"2024-12-16","cited_by_count":2,"area":null},{"id":"2504.03387","title":"BitHEP -- The Limits of Low-Precision ML in HEP","created":"2025-04-04","cited_by_count":1,"area":null},{"id":"2609.06686","title":"Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events","created":"2026-09-06","cited_by_count":0,"area":null}],"next_cursor":null}