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Neural Networks are ubiquitous in high energy physics research.
Using auc and accuracy in evaluating learning algorithms
Jin Huang and Charles X Ling · 2005
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The nova technical design report
DS Ayres, GR Drake, MC Goodman, JJ Grudzinski, VJ Guarino, RL Talaga, A Zhao, P Stamoulis, E Stiliaris, G Tzanakos, et al · 2007
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Online particle detection with neural networks based on topological calorimetry information
T Ciodaro, D Deva, JM De Seixas, and D Damazio · 2012
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Near-intrinsic energy resolution for 30–662 kev gamma rays in a high pressure xenon electroluminescent tpc
V Álvarez, FIGM Borges, S Cárcel, J Castel, S Cebrián, A Cervera, Carlos AN Conde, Theopisti Dafni, THVT Dias, J Díaz, et al · 2013
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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A convolutional neural network neutrino event classifier
Adam Aurisano, Alexander Radovic, D Rocco, Alexander Himmel, MD Messier, E Niner, G Pawloski, Fernanda Psihas, Alexandre Sousa, and P Vahle · 2016
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Background rejection in next using deep neural networks
Joshua Renner, A Farbin, J Muñoz Vidal, JM Benlloch-Rodríguez, A Botas, Paola Ferrario, Juan José Gómez-Cadenas, Vicente Alvarez, CDR Azevedo, FIG Borges, et al · 2017
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Deep generative models for fast shower simulation in atlas
Dalila Salamani, Stefan Gadatsch, Tobias Golling, Graeme Andrew Stewart, Aishik Ghosh, David Rousseau, Ahmed Hasib, and Jana Schaarschmidt · 2018
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Fast inference of deep neural networks in fpgas for particle physics
Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan Rivera, Nhan Tran, et al · 2018
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Generative models for fast simulation
Sofia Vallecorsa · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Xai—explainable artificial intelligence
David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang · 2019
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Towards a new generation of parton densities with deep learning models
Stefano Carrazza and Juan Cruz-Martinez · 2019
Cited alongside, same era.
Atlas b-jet identification performance and efficiency measurement with t t ¯ t\overline{t} events in pp collisions at s = 13 \sqrt{s}=13 tev
The ATLAS Collaboration · 2019
Cited alongside, same era.
Lhc analysis-specific datasets with generative adversarial networks
Bobak Hashemi, Nick Amin, Kaustuv Datta, Dominick Olivito, and Maurizio Pierini · 2019
Cited alongside, same era.
Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
Cited alongside, same era.
Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
Cited alongside, same era.
A review on machine learning for neutrino experiments
Fernanda Psihas, Micah Groh, Christopher Tunnell, and Karl Warburton · 2020
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Interaction networks for the identification of boosted h→ b b decays
Eric A Moreno, Thong Q Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B Newman, Avikar Periwal, Maria Spiropulu, Javier M Duarte, and Maurizio Pierini · 2020
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Explainable artificial intelligence for tabular data: A survey
Maria Sahakyan, Zeyar Aung, and Talal Rahwan · 2021
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Explaining machine-learned particle-flow reconstruction
Farouk Mokhtar, Raghav Kansal, Daniel Diaz, Javier Duarte, Joosep Pata, Maurizio Pierini, and Jean-Roch Vlimant · 2021
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Mlpf: efficient machine-learned particle-flow reconstruction using graph neural networks
Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, and Maria Spiropulu · 2021
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Reweighting a parton shower using a neural network: the final-state case
Enrico Bothmann and Luigi Del Debbio · 2019
Cited alongside, same era.
The machine learning landscape of top taggers
Gregor Kasieczka, Tilman Plehn, Anja Butter, Kyle Cranmer, Dipsikha Debnath, Barry M Dillon, Malcolm Fairbairn, Darius A Faroughy, Wojtek Fedorko, Christophe Gay, et al · 2019
Cited alongside, same era.
Explainable ai: a review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis · 2020
Cited alongside, same era.
Explainable artificial intelligence: a systematic review
Giulia Vilone and Luca Longo · 2020
Cited alongside, same era.
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2020
Cited alongside, same era.
A survey of interpretability of machine learning in accelerator-based high energy physics
Danielle Turvill, Lee Barnby, Bo Yuan, and Ali Zahir · 2020
Cited alongside, same era.
Accelerated charged particle tracking with graph neural networks on fpgas
Aneesh Heintz, Vesal Razavimaleki, Javier Duarte, Gage DeZoort, Isobel Ojalvo, Savannah Thais, Markus Atkinson, Mark Neubauer, Lindsey Gray, Sergo Jindariani, et al · 2020
Cited alongside, same era.
Distance-weighted graph neural networks on fpgas for real-time particle reconstruction in high energy physics
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, et al · 2021
Later among the works it cites.
An open-source machine learning framework for global analyses of parton distributions
Richard D Ball, Stefano Carrazza, Juan Cruz-Martinez, Luigi Del Debbio, Stefano Forte, Tommaso Giani, Shayan Iranipour, Zahari Kassabov, Jose I Latorre, Emanuele R Nocera, et al · 2021
Later among the works it cites.
Bridging the gap between explainable ai and uncertainty quantification to enhance trustability
Dominik Seuß · 2021
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Explainable machine learning of the underlying physics of high-energy particle collisions
Yue Shi Lai, Duff Neill, Mateusz Płoskoń, and Felix Ringer · 2022
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Graph neural networks in particle physics: Implementations, innovations, and challenges
Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S Neubauer, and Kazuhiro Terao · 2022
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Lessons on interpretable machine learning from particle physics
Christophe Grojean, Ayan Paul, Zhuoni Qian, and Inga Strümke · 2022
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