Fetching the paper…
Reading the bibliography…
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking.
1902
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1905
Earlier work this paper cites.
1912
Earlier work this paper cites.
R. O. Duda and P. E. Hart, “Use of the hough transformation to detect lines and curves in pictures,” Commun. ACM
1972
Earlier work this paper cites.
AAAI Press, 1996
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Kdd · 1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput
1997
Earlier work this paper cites.
Association for Computing Machinery, New York, NY, USA, 1998
P. Indyk and R. Motwani, “Approximate nearest neighbors: Towards removing the curse of dimensionality,” in Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing · 1998
Earlier work this paper cites.
2005
Earlier work this paper cites.
2007
Earlier work this paper cites.
A. Strandlie and R. Frühwirth, “Track and vertex reconstruction: From classical to adaptive methods,” Rev. Mod. Phys
2010
Earlier work this paper cites.
JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA, 11–13 apr, 2011
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics · 2011
Earlier work this paper cites.
A. Heintz et al · 2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
2012
Cited alongside, same era.
D. Funke, T. Hauth, V. Innocente, G. Quast, P. Sanders, and D. Schieferdecker, “Parallel track reconstruction in CMS using the cellular automaton approach,” J. Phys. Conf. Ser
2014
Cited alongside, same era.
K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” 2014
2014
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2015
2015
Cited alongside, same era.
https://heptrkx.github.io/
HEP.TrkX, “HEP advanced tracking algorithms with cross-cutting applications,” 2016 · 2016
2017
Later among the works it cites.
2018
Later among the works it cites.
A. Tsaris, D. Anderson, J. Bendavid, P. Calafiura, G. Cerati, J. Esseiva, S. Farrell, L. Gray, K. Kapoor, J. Kowalkowski, M. Mudigonda, Prabhat, P. Spentzouris, M. Spiropoulou, J.-R. Vlimant, S. Zheng, and D. Zurawski, “The HEP.TrkX project: Deep learning for particle tracking,” Journal of Physics: Conference Series
2018
Later among the works it cites.
https://cds.cern.ch/record/2693670
ATLAS Collaboration, “Fast Track Reconstruction for HL-LHC,” Tech. Rep. ATL-PHYS-PUB-2019-041, CERN, Geneva, Oct, 2019 · 2019
Later among the works it cites.
https://exatrkx.github.io/
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Cited alongside, same era.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ComputingandSoftwarePublicResults
ATLAS Collaboration, “ Computing and Software Public Results,” 2017 · 2017
Cited alongside, same era.
https://twiki.cern.ch/twiki/bin/view/CMSPublic/TrackingPOGPerformance2017MC
CMS Collaboration, “ CMS Tracking POG Performance Plots For 2017 with PhaseI pixel detector,” 2017 · 2017
Cited alongside, same era.
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: Going beyond euclidean data,” IEEE Signal Processing Magazine
2017
Cited alongside, same era.
https://cds.cern.ch/record/2285585
ATLAS Collaboration, “Technical Design Report for the ATLAS Inner Tracker Pixel Detector,” Tech. Rep. CERN-LHCC-2017-021. ATLAS-TDR-030, CERN, Geneva, Sep, 2017 · 2017
Cited alongside, same era.
A. Collaboration, “Technical Design Report for the ATLAS Inner Tracker Pixel Detector,” Tech. Rep. ATLAS-TDR-030, CERN, Geneva, Sept., 2017
2017
Cited alongside, same era.
Exa.TrkX, “ HEP advanced tracking algorithms at the exascale,” 2019 · 2019
Later among the works it cites.
https://cds.cern.ch/record/2669540
ATLAS Collaboration, “Expected Tracking Performance of the ATLAS Inner Tracker at the HL-LHC,” Tech. Rep. ATL-PHYS-PUB-2019-014, CERN, Geneva, Mar, 2019 · 2019
Later among the works it cites.
I. B. Alonso, O. Brüning, P. Fessia, M. Lamont, L. Rossi, L. Tavian, and M. Zerlauth, “High Luminosity Large Hadron Collider HL-LHC Technical Design Report,” CERN Yellow Report
2020
Later among the works it cites.
V. Kuznetsov, L. Giommi, and D. Bonacorsi, “Mlaas4hep: Machine learning as a service for hep,” 2020
2020
Later among the works it cites.
S. Scardapane, I. Spinelli, and P. D. Lorenzo, “Distributed training of graph convolutional networks,” IEEE Transactions on Signal and Information Processing over Networks
2020
Later among the works it cites.
V Hewes, A. Aurisano, G. Cerati, J. Kowalkowski, C. Lee, W. keng Liao, A. Day, A. Agrawal, M. Spiropulu, J.-R. Vlimant, L. Gray, T. Klijnsma, P. Calafiura, S. Conlon, S. Farrell, X. Ju, and D. Murnane, “Graph neural network for object reconstruction in liquid argon time projection chambers,” 2021
2021
Closest in time.
Accessed 2021-03-01
“CuGraph.” https://github.com/rapidsai/cugraph , 2020 · 2021
Closest in time.
Springer US, New York, NY, 2021
D. Chicco, Siamese Neural Networks: An Overview · 2021
Closest in time.
F. Fahim, B. Hawks, C. Herwig, J. Hirschauer, S. Jindariani, N. Tran, L. P. Carloni, G. D. Guglielmo, P. Harris, J. Krupa, D. Rankin, M. B. Valentin, J. Hester, Y. Luo, J. Mamish, S. Orgrenci-Memik, T. Aarrestad, H. Javed, V. Loncar, M. Pierini, A. A. Pol, S. Summers, J. Duarte, S. Hauck, S.-C. Hsu, J. Ngadiuba, M. Liu, D. Hoang, E. Kreinar, and Z. Wu, “hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices,” 2021
2021
Closest in time.
J. Krupa, K. Lin, M. Acosta Flechas, J. Dinsmore, J. Duarte, P. Harris, S. Hauck, B. Holzman, S.-C. Hsu, T. Klijnsma, and et al., “Gpu coprocessors as a service for deep learning inference in high energy physics,” Machine Learning: Science and Technology
2021
Closest in time.
Accessed 2021-03-01
“NVIDIA TensorRT.” https://docs.nvidia.com/deeplearning/tensorrt/index.html , 2020 · 2021
Closest in time.