{"total":27,"rows":[{"id":"2112.03769","title":"Machine Learning in the Search for New Fundamental Physics","created":"2021-12-07","cited_by_count":50,"area":null},{"id":"2106.05285","title":"CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows","created":"2021-06-09","cited_by_count":46,"area":null},{"id":"2101.08578","title":"MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks","created":"2021-01-21","cited_by_count":36,"area":null},{"id":"2411.00446","title":"A Lorentz-Equivariant Transformer for All of the LHC","created":"2024-11-01","cited_by_count":28,"area":null},{"id":"2110.13632","title":"Generative Networks for Precision Enthusiasts","created":"2021-10-22","cited_by_count":21,"area":null},{"id":"2212.08674","title":"An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training","created":"2022-12-16","cited_by_count":20,"area":null},{"id":"2404.18807","title":"The Landscape of Unfolding with Machine Learning","created":"2024-04-29","cited_by_count":20,"area":null},{"id":"2110.06933","title":"Style-based quantum generative adversarial networks for Monte Carlo events","created":"2021-10-13","cited_by_count":19,"area":null},{"id":"2312.09597","title":"Deep Generative Models for Detector Signature Simulation: A Taxonomic Review","created":"2023-12-15","cited_by_count":18,"area":null},{"id":"2104.04543","title":"Understanding Event-Generation Networks via Uncertainties","created":"2021-04-09","cited_by_count":17,"area":null},{"id":"2106.00792","title":"Latent Space Refinement for Deep Generative Models","created":"2021-06-01","cited_by_count":10,"area":null},{"id":"2105.04448","title":"Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution","created":"2021-05-10","cited_by_count":9,"area":null},{"id":"2404.14332","title":"Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion","created":"2024-04-22","cited_by_count":8,"area":null},{"id":"2106.00643","title":"A survey of machine learning-based physics event generation","created":"2021-06-01","cited_by_count":6,"area":null},{"id":"2404.18992","title":"Unifying Simulation and Inference with Normalizing Flows","created":"2024-04-29","cited_by_count":6,"area":null},{"id":"2411.02495","title":"Generative Unfolding with Distribution Mapping","created":"2024-11-04","cited_by_count":6,"area":null},{"id":"2601.13308","title":"Scaling laws for amplitude surrogates","created":"2026-01-19","cited_by_count":6,"area":null},{"id":"2302.05390","title":"Unbinned Profiled Unfolding","created":"2023-02-10","cited_by_count":5,"area":null},{"id":"2507.15084","title":"Simulation-Prior Independent Neural Unfolding Procedure","created":"2025-07-20","cited_by_count":4,"area":null},{"id":"2302.12906","title":"Generative Invertible Quantum Neural Networks","created":"2023-02-24","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":"2605.30453","title":"Generative Models and Statistical Validation","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2605.02014","title":"MIRA: A Score for Conditional Distribution Accuracy and Model Comparison","created":"2026-05-03","cited_by_count":1,"area":null},{"id":"2310.08717","title":"Designing Observables for Measurements with Deep Learning","created":"2023-10-12","cited_by_count":0,"area":null},{"id":"2401.05295","title":"Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2512.07074","title":"Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters","created":"2025-12-08","cited_by_count":0,"area":null},{"id":"2601.22029","title":"Inverse Problems Conditioned on Observation Ensembles: Applications and Methods","created":"2026-01-29","cited_by_count":0,"area":null}],"next_cursor":null}