Bibliography

[1] Z. Li, W. Yang, S. Peng, F. Liu, A Survey of Convolutional Neural Networks:
Analysis, Applications, and Prospects, abs/2004.02806, 2020

[2] Z. Zhang, P. Cui, W. Zhu, Deep Learning on Graphs: A Survey, abs/1812.04202, 2020

[3] S.R. Qasim, J. Kieseler, Y. Iiyama, and M. Pierini, Learning representations of
irregular particle-detector geometry with distance-weighted graph networks. The
European Physical Journal C, 79(7), abs/1902.07987, 2019

[4] N. Choma, F. Monti, L. Gerhardt, T. Palczewski, Z. Ronaghi, M. Prabhat, W.
Bhimji, M.M. Bronstein, S.R. Klein, and J. Bruna, Graph neural networks for
Icecube signal classification. CoRR, abs/1809.06166, 2018

[5] CMS Collaboration, The Phase-2 Upgrade of the CMS Endcap Calorimeter.
Technical Report CERN-LHCC-2017-023. CMS-TDR-019, 2017

[6] A. Kazi, S. Albarqouni, K. Kortuem, N. Navab, Multi Layered-Parallel Graph
Convolutional Network (ML-PGCN) for Disease Prediction, abs/1804.10776, 2018

[7] H. Alemdar, V. Leroy, A. Prost-Boucle, F. Pétrot, Ternary Neural Networks for
Resource-Efficient AI Applications, abs/1609.00222, 2016

[8] John F. Beacom, The Diffuse Supernova Neutrino Background, Ann.Rev.Nucl.Part.Sci.60:439-462, abs/1004.3311, 2010

[9] Super-Kamiokande collaboration, Supernova Relic Neutrino search with
neutron tagging at Super-Kamiokande-IV , Astroparticle Physics 60, 41–46,
abs/1311.3738, 2015

[10] Y. Iiyama et al, Distance-Weighted Graph Neural Networks on FPGAs for
Real-Time Particle Reconstruction in High Energy Physics, Frontiers in Big Data 3,
44, abs/2008.03601, 2021

[11] A. Di Pilato et al, Reconstruction in an imaging calorimeter for HL-LHC, JINST
15 06, C06023, abs/2004.10027, 2020

[12] Gilmer et al, Neural Message Passing for Quantum Chemistry, abs/1704.01212, 2017

[13] Xie T. and Grossman J.C., Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties, abs/1710.10324, 2018

[14] Zhang M. et al, An End-to-End Deep Learning Architecture for Graph Classification, link, 2018