Explainable AI for High Energy Physics

14 June 2022

Papers citing "Explainable AI for High Energy Physics"

9 / 9 papers shown

Title
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class Hongbo Zhu Angelo Cangelosi Procheta Sen Anirbit Mukherjee FAtt 42 0 0 07 Oct 2023
Two-step interpretable modeling of Intensive Care Acquired Infections Giacomo Lancia Meri Varkila O. Cremer C. Spitoni 15 0 0 26 Jan 2023
FAIR AI Models in High Energy Physics Javier Mauricio Duarte Haoyang Li Avik Roy Ruike Zhu Eliu A. Huerta ... Mark S. Neubauer Sang Eon Park M. Quinnan R. Rusack Zhizhen Zhao 30 8 0 09 Dec 2022
Do graph neural networks learn traditional jet substructure? Farouk Mokhtar Raghav Kansal Javier Mauricio Duarte GNN 34 11 0 17 Nov 2022
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers Ayush Khot Mark S. Neubauer Avik Roy AAML 33 16 0 09 Oct 2022
FAIR for AI: An interdisciplinary and international community building perspective Eliu A. Huerta B. Blaiszik L. C. Brinson Kristofer E Bouchard Daniel Madrigal Diaz ... F. Psomopoulos Avik Roy Oliver Rübel Zhizhen Zhao Ruike Zhu 32 41 0 30 Sep 2022
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks J. Pata Javier Mauricio Duarte J. Vlimant M. Pierini M. Spiropulu 107 76 0 21 Jan 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 167 592 0 31 Dec 2020
Fast inference of deep neural networks in FPGAs for particle physics Javier Mauricio Duarte Song Han Philip C. Harris S. Jindariani E. Kreinar ... J. Ngadiuba M. Pierini R. Rivera N. Tran Zhenbin Wu AI4CE 75 386 0 16 Apr 2018