{"total":42,"rows":[{"id":"2102.05034","title":"SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks","created":"2021-02-09","cited_by_count":110,"area":null},{"id":"2102.02770","title":"A Living Review of Machine Learning for Particle Physics","created":"2021-02-02","cited_by_count":78,"area":null},{"id":"2209.03299","title":"Multimodal learning with graphs","created":"2022-09-07","cited_by_count":54,"area":null},{"id":"2303.06949","title":"Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling","created":"2023-03-13","cited_by_count":41,"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":"2305.04847","title":"CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation","created":"2023-05-08","cited_by_count":33,"area":null},{"id":"2110.13041","title":"Applications and Techniques for Fast Machine Learning in Science","created":"2021-10-25","cited_by_count":32,"area":null},{"id":"2103.06995","title":"Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking","created":"2021-03-11","cited_by_count":30,"area":null},{"id":"2102.05073","title":"Point Cloud Transformers applied to Collider Physics","created":"2021-02-09","cited_by_count":23,"area":null},{"id":"2301.08128","title":"EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets","created":"2023-01-17","cited_by_count":23,"area":null},{"id":"2103.16701","title":"Charged particle tracking via edge-classifying interaction networks","created":"2021-03-30","cited_by_count":19,"area":null},{"id":"2104.14272","title":"Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks","created":"2021-04-29","cited_by_count":18,"area":null},{"id":"2112.02048","title":"Graph Neural Networks for Charged Particle Tracking on FPGAs","created":"2021-12-03","cited_by_count":17,"area":null},{"id":"2203.00330","title":"Machine Learning for Particle Flow Reconstruction at CMS","created":"2022-03-01","cited_by_count":14,"area":null},{"id":"2111.12840","title":"Explaining machine-learned particle-flow reconstruction","created":"2021-11-24","cited_by_count":12,"area":null},{"id":"2203.12852","title":"Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2209.14065","title":"LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics","created":"2022-09-28","cited_by_count":12,"area":null},{"id":"2402.12535","title":"Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics","created":"2024-02-19","cited_by_count":12,"area":null},{"id":"2111.12849","title":"Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance","created":"2021-11-24","cited_by_count":10,"area":null},{"id":"2312.03823","title":"High Pileup Particle Tracking with Object Condensation","created":"2023-12-06","cited_by_count":8,"area":null},{"id":"2202.12586","title":"Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting","created":"2022-02-25","cited_by_count":7,"area":null},{"id":"2303.01640","title":"Hierarchical Graph Neural Networks for Particle Track Reconstruction","created":"2023-03-03","cited_by_count":7,"area":null},{"id":"2204.01681","title":"End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks","created":"2022-04-04","cited_by_count":6,"area":null},{"id":"2508.14087","title":"FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics","created":"2025-08-13","cited_by_count":6,"area":null},{"id":"2509.24770","title":"Neural Message-Passing on Attention Graphs for Hallucination Detection","created":"2025-09-29","cited_by_count":6,"area":null},{"id":"2107.00656","title":"Shared Data and Algorithms for Deep Learning in Fundamental Physics","created":"2021-07-01","cited_by_count":4,"area":null},{"id":"2211.09912","title":"Do graph neural networks learn traditional jet substructure?","created":"2022-11-17","cited_by_count":4,"area":null},{"id":"2309.14027","title":"TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography","created":"2023-09-25","cited_by_count":4,"area":null},{"id":"2311.04190","title":"Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter","created":"2023-11-07","cited_by_count":4,"area":null},{"id":"2210.13869","title":"Jet tagging algorithm of graph network with HaarPooling message passing","created":"2022-10-25","cited_by_count":3,"area":null},{"id":"2303.02101","title":"Configurable calorimeter simulation for AI applications","created":"2023-03-03","cited_by_count":3,"area":null},{"id":"2104.01725","title":"Graph Generative Models for Fast Detector Simulations in High Energy Physics","created":"2021-04-05","cited_by_count":2,"area":null},{"id":"2104.07182","title":"Convolutions for Spatial Interaction Modeling","created":"2021-04-15","cited_by_count":2,"area":null},{"id":"2304.11810","title":"PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis","created":"2023-04-24","cited_by_count":2,"area":null},{"id":"2307.16662","title":"Graph Structure from Point Clouds: Geometric Attention is All You Need","created":"2023-07-31","cited_by_count":2,"area":null},{"id":"2407.13925","title":"EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction","created":"2024-07-18","cited_by_count":1,"area":null},{"id":"2208.05952","title":"Partition Pooling for Convolutional Graph Network Applications in Particle Physics","created":"2022-08-11","cited_by_count":0,"area":null},{"id":"2408.10599","title":"Vision Calorimeter for Anti-neutron Reconstruction: A Baseline","created":"2024-08-20","cited_by_count":0,"area":null},{"id":"2409.16052","title":"Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction","created":"2024-09-24","cited_by_count":0,"area":null},{"id":"2410.07250","title":"Lightweight Deep Learning Framework for Accurate Particle Flow Energy Reconstruction","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2509.16287","title":"Architectural change in neural networks using fuzzy vertex pooling","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2606.20437","title":"HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction","created":"2026-06-18","cited_by_count":0,"area":null}],"next_cursor":null}