图书简介

Discriminative Models for Signal/Background Boosting: Boosted Decision Trees (Y Coadou); Deep Learning from Four-Vectors (P Baldi, P Sadowski, and D Whiteson); Anomaly Detection for Physics Analysis and Less than Supervised Learning (B Nachman); Data Quality Monitoring: Data Quality Monitoring Anomaly Detection (A Pol, G Carminara, C Germain, and M Pierini); Generative Models: Generative Models for Fast Simulation (M Paganini et al.); Generative Networks for LHC Events (A Butter and T Plehn); Machine Learning Platforms: Distributed Training and Optimization of Neural Networks (J R Vlimant and J Yin); Machine Learning for Triggering and Data Acquisition (P Harris); Detector Data Reconstruction: End-to-End Analysis using Image Classification (A Aurisano and L Whitehead); Clustering (K Terao); Graph Neural Networks for Particle Tracking and Reconstruction (J Duarte and J R Vlimant); Jet Classification and Particle Identification from Low Level: Sequence-Based Learning (R Teixeira de Lima); Particle Identification in Neutrino Detectors (R Sharankova and T Wongjirad); Image-Based Jet Analysis (M Kagan); Physics Inference: Simulation-Based Inference Methods for Particle Physics (J Brehmer and K Cranmer); Dealing with Nuisance Parameters (T Dorigo and P de Castro Manzano); Bayesian Neural Networks (T Charnock, L Perreault-Levasseur, and F Lanusse); Parton Distribution Functions (S Forte and S Carrazza); Machine Learning Challenges: Machine Learning Challenges and Open Data Sets (D Rousseau and A Uztyushanin);

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