Machine Learning (ML) will play significant role in success of the upcoming High-Luminosity LHC (HL-LHC) program at CERN. The unprecedented amount of data at the Exa-Byte scale to be collected by the CERN experiments in next decade will require a novel approaches to train and use ML models. In this paper we discuss Machine Learning as a Service (MLaaS) model which is capable to read HEP data in their native ROOT data format, rely on the World-Wide LHC Grid (WLCG) infrastructure for remote data access, and serve a pre-trained model via HTTP protocol. Such modular design opens up a possibility to train data at large scale by reading ROOT files from remote storages, avoiding data-transformation to flatten data formats currently used by ML frameworks, and easily access pre-trained ML models in existing infrastructure and applications.
Results of the analysis of a survey for young scientists on training quality in HEP instrumentation software and machine learning
University of Torino and INFN, Turin（都灵大学和意大利国家核物理研究所）；Italy（巴塞罗那高等物理研究所）；Institut de Física d’Altes Energies, Barcelona（根特大学）；Spain（波兰科学院亨利克·涅沃德尼茨基核物理研究所）；Ghent University, Ghent（热那亚大学和意大利国家核物理研究所）；Belgium（卡托维兹西里西亚大学）；Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, Kraków；Poland；University of Genoa and INFN, Genoa；Italy；University of Silesia in Katowice, Katowice；Poland
Machine Can Automatically Discover Parametric Functions to Model HEP Data
University of Pennsylvania, USA（宾夕法尼亚大学）；European Organization for Nuclear Research (CERN), Switzerland（欧洲核子研究中心）；University of Cambridge, UK（剑桥大学）；University of Wisconsin-Madison, USA（威斯康星大学麦迪逊分校）；University of California San Diego, USA（加州大学圣地亚哥分校）；Massachusetts Institute of Technology, USA（麻省理工学院）；Institute for Artificial Intelligence（人工智能研究所）
Brown University（布朗大学）；Columbia University（哥伦比亚大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；California Institute of Technology（加州理工学院）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；Universität Hamburg（汉堡大学）；European Organization for Nuclear Research（欧洲核子研究组织）；Purdue University（普渡大学）；University of Puerto Rico Mayagüez（波多黎各大学马亚圭斯分校）；University of Colorado Boulder（科罗拉多大学博尔德分校）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Princeton University（普林斯顿大学）；Laboratório de Instrumentação e Física Experimental de Partículas (LIP)（粒子仪器与实验物理实验室）
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Princeton University（普林斯顿大学）；Lawrence Berkeley National Lab（劳伦斯伯克利国家实验室）；Laboratório de Instrumentação e Física Experimental de Partículas (LIP)（粒子物理实验与仪器实验室（LIP））；University of California San Diego（加州大学圣地亚哥分校）；ICEPP, University of Tokyo（东京大学基本粒子物理国际研究中心（ICEPP））；Stanford Linear Accelerator Laboratory（斯坦福直线加速器中心实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
The Living Guide of Machine Learning for Particle Physics
Marietta Blau Institute for Particle Physics, Austrian Academy of Sciences（玛丽塔·布劳粒子物理研究所，奥地利科学院）；Università degli Studi di Milano & INFN Sezione di Milano（米兰大学及意大利国家核物理研究所米兰分部）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Department of Particle Physics and Astrophysics, Stanford University（斯坦福大学粒子物理与天体物理学系）
