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Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces.
A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
LB ALMEIDA · 1987
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Generalization of back-propagation to recurrent neural networks
Fernando J Pineda · 1987
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Rave—a detector-independent toolkit to reconstruct vertices
W. Waltenberger · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Convolutional neural networks over tree structures for programming language processing, 2014
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Pileup Per Particle Identification
Daniele Bertolini, Philip Harris, Matthew Low, and Nhan Tran · 2014
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Gated graph sequence neural networks, 2015
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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Interaction networks for learning about objects, relations and physics, 2016
Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Neural message passing for quantum chemistry, 2017
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Deep-learning Top Taggers or The End of QCD?
Gregor Kasieczka, Tilman Plehn, Michael Russell, and Torben Schell · 2017
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Identification of Jets Containing b b -Hadrons with Recurrent Neural Networks at the ATLAS Experiment
ATLAS Collaboration · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Particle-flow reconstruction and global event description with the CMS detector
A.M. Sirunyan et al · 2017
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Neural message passing for jet physics
J. Bruna K. Cho K. Cranmer G. Louppe et al. I. Henrion, J. Brehmer · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Machine learning at the energy and intensity frontiers of particle physics
Alexander Radovic, Mike Williams, David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao, and Taritree Wongjirad · 2018
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Deep Learning and its Application to LHC Physics
Dan Guest, Kyle Cranmer, and Daniel Whiteson · 2018
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Ordered neurons: Integrating tree structures into recurrent neural networks, 2018
Yikang Shen, Shawn Tan, Alessandro Sordoni, and Aaron Courville · 2018
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Relational inductive biases, deep learning, and graph networks, 2018
P. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Pulling Out All the Tops with Computer Vision and Deep Learning
Sebastian Macaluso and David Shih · 2018
Particlenet: Jet tagging via particle clouds, 2019
Huilin Qu and Loukas Gouskos · 2019
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Interaction networks for the identification of boosted h → b b ¯ h\to b\overline{b} decays, 2019
Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, and Maurizio Pierini · 2019
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Probing stop pair production at the LHC with graph neural networks
Murat Abdughani, Jie Ren, Lei Wu, and Jin Min Yang · 2019
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Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama, and Maurizio Pierini · 2019
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Structured agents for physical construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L Stachenfeld, Pushmeet Kohli, Peter W Battaglia, and Jessica B Hamrick · 2019
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Cited alongside, same era.
Boosting H → b b ¯ H\to b\bar{b} with Machine Learning
Joshua Lin, Marat Freytsis, Ian Moult, and Benjamin Nachman · 2018
Cited alongside, same era.
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
Cited alongside, same era.
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2018
Cited alongside, same era.
Review of Particle Physics
M. Tanabashi et al · 2018
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds, 2018
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2018
Cited alongside, same era.
Deep-learned Top Tagging with a Lorentz Layer
Anja Butter, Gregor Kasieczka, Tilman Plehn, and Michael Russell · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Machine and Deep Learning Applications in Particle Physics
Dimitri Bourilkov · 2020
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Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
Andrew J. Larkoski, Ian Moult, and Benjamin Nachman · 2020
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A living review of machine learning for particle physics https://iml-wg.github.io/HEPML-LivingReview/ , 2020
HEP Community · 2020
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Discovering symbolic models from deep learning with inductive biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho · 2020
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End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
M. Andrews, M. Paulini, S. Gleyzer, and B. Poczos · 2020
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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
CMS Collaboration · 2020
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Technical Report ATL-PHYS-PUB-2020-014, CERN, Geneva, May 2020
Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W Battaglia · 2020
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Lagrangian fluid simulation with continuous convolutions
Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, and Vladlen Koltun · 2020
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Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2020
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Jedi-net: a jet identification algorithm based on interaction networks
Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, and Jean-Roch Vlimant · 2020
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Abcnet: An attention-based method for particle tagging, 2020
Vinicius Mikuni and Florencia Canelli · 2020
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Casting a graph net to catch dark showers
Elias Bernreuther, Thorben Finke, Felix Kahlhoefer, Michael Krämer, and Alexander Mück · 2020
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Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
Jie Ren, Lei Wu, and Jin Min Yang · 2020
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Probing triple Higgs coupling with machine learning at the LHC
Murat Abdughani, Daohan Wang, Lei Wu, Jin Min Yang, and Jun Zhao · 2020
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Jan Kieseler · 2020
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Efficiency parameterization with neural networks
C. Badiali, F. A. Di Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, and J. Shlomi · 2020
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Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Xiangyang Ju et al · 2020
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Set2graph: Learning graphs from sets, 2020
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, and Yaron Lipman · 2020
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Learning graph structure with a finite-state automaton layer
Daniel D Johnson, Hugo Larochelle, and Daniel Tarlow · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, SM Eslami, and Peter W Battaglia · 2020
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