Built on

B. H. Denby, Neural Networks and Cellular Automata in Experimental High-energy Physics , Comput. Phys. Commun. 49
1988
Earlier work this paper cites.
L. Lonnblad, C. Peterson and T. Rognvaldsson, Finding Gluon Jets With a Neural Trigger , Phys. Rev. Lett. 65
1990
Earlier work this paper cites.
L. Lonnblad, C. Peterson and T. Rognvaldsson, Using neural networks to identify jets , Nucl. Phys. B349
1991
Earlier work this paper cites.
P. Chiappetta, P. Colangelo, P. De Felice, G. Nardulli and G. Pasquariello, Higgs search by neural networks at LHC , Phys. Lett. B322
1994
Earlier work this paper cites.
C. Goller and A. Kuchler, Learning task-dependent distributed representations by backpropagation through structure , in Neural Networks, 1996., IEEE International Conference on , vol. 1, pp. 347–352, IEEE, 1996
1996
Earlier work this paper cites.
R. Sinkus and T. Voss, Particle identification with neural networks using a rotational invariant moment representation , Nucl. Instrum. Meth. A391
1997
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
G. P. Salam, Towards Jetography , Eur. Phys. J. C67
Original
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines , in Proceedings of the 27th international conference on machine learning (ICML-10) , pp. 807–814, 2010
2010
Earlier work this paper cites.
R. Socher, C. C. Lin, C. Manning and A. Y. Ng, Parsing natural scenes and natural language with recursive neural networks , in Proceedings of the 28th international conference on machine learning (ICML-11) , pp. 129–136, 2011
2011
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R. Socher, J. Pennington, E. H. Huang, A. Y. Ng and C. D. Manning, Semi-supervised recursive autoencoders for predicting sentiment distributions , in Proceedings of the Conference on Empirical Methods in Natural Language Processing , pp. 151–161, Association for Computational Linguistics, 2011
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness , JHEP 03
Original
2011
Earlier work this paper cites.
M. Ritzmann, D. A. Kosower and P. Skands, Antenna Showers with Hadronic Initial States , Phys. Lett. B718
Original
2013
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam and Y. LeCun, Spectral networks and locally connected networks on graphs , CoRR abs/1312.6203
Original
2013
Earlier work this paper cites.
K. Cho, B. van Merriënboer, D. Bahdanau and Y. Bengio, On the properties of neural machine translation: Encoder-decoder approaches , arXiv preprint arXiv:1409.1259 (2014)
Original
2014
Earlier work this paper cites.

Similar

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk et al., Learning phrase representations using rnn encoder-decoder for statistical machine translation , arXiv preprint arXiv:1406.1078 (2014)
Original
2014
Cited alongside, same era.
J. Chung, C. Gulcehre, K. Cho and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling , arXiv preprint arXiv:1412.3555 (2014)
Original
2014
Cited alongside, same era.
D. Kingma and J. Ba, Adam: A method for stochastic optimization , arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Cited alongside, same era.
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging , JHEP 02
Original
2015
Cited alongside, same era.

Then

G. Louppe, M. Kagan and K. Cranmer, Learning to Pivot with Adversarial Networks , arXiv preprint arXiv:1611.01046 (2016)
Original
2016
Later among the works it cites.
S. R. Bowman, J. Gauthier, A. Rastogi, R. Gupta, C. D. Manning and C. Potts, A fast unified model for parsing and sentence understanding , arXiv preprint arXiv:1603.06021 (2016)
Original
2016
Later among the works it cites.
D. Yogatama, P. Blunsom, C. Dyer, E. Grefenstette and W. Ling, Learning to compose words into sentences with reinforcement learning , arXiv preprint arXiv:1611.09100 (2016)
Original
2016
Later among the works it cites.
M. Niepert, M. Ahmed and K. Kutzkov, Learning convolutional neural networks for graphs , CoRR abs/1605.05273
Original
2016
Later among the works it cites.

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2017

QCD-Aware Recursive Neural Networks for Jet Physics

Louppe, Gilles, Cho, Kyunghyun, Becot, Cyril et al.

Understand

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images.

In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages.
In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence.
Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based tree structure varies on an event-by-event basis.

Built on

B. H. Denby, Neural Networks and Cellular Automata in Experimental High-energy Physics , Comput. Phys. Commun. 49
1988
Earlier work this paper cites.
L. Lonnblad, C. Peterson and T. Rognvaldsson, Finding Gluon Jets With a Neural Trigger , Phys. Rev. Lett. 65
1990
Earlier work this paper cites.
L. Lonnblad, C. Peterson and T. Rognvaldsson, Using neural networks to identify jets , Nucl. Phys. B349
1991
Earlier work this paper cites.
P. Chiappetta, P. Colangelo, P. De Felice, G. Nardulli and G. Pasquariello, Higgs search by neural networks at LHC , Phys. Lett. B322
1994
Earlier work this paper cites.
C. Goller and A. Kuchler, Learning task-dependent distributed representations by backpropagation through structure , in Neural Networks, 1996., IEEE International Conference on , vol. 1, pp. 347–352, IEEE, 1996
1996
Earlier work this paper cites.
R. Sinkus and T. Voss, Particle identification with neural networks using a rotational invariant moment representation , Nucl. Instrum. Meth. A391
1997
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
G. P. Salam, Towards Jetography , Eur. Phys. J. C67
Original
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines , in Proceedings of the 27th international conference on machine learning (ICML-10) , pp. 807–814, 2010
2010
Earlier work this paper cites.
R. Socher, C. C. Lin, C. Manning and A. Y. Ng, Parsing natural scenes and natural language with recursive neural networks , in Proceedings of the 28th international conference on machine learning (ICML-11) , pp. 129–136, 2011
2011
Earlier work this paper cites.
R. Socher, J. Pennington, E. H. Huang, A. Y. Ng and C. D. Manning, Semi-supervised recursive autoencoders for predicting sentiment distributions , in Proceedings of the Conference on Empirical Methods in Natural Language Processing , pp. 151–161, Association for Computational Linguistics, 2011
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness , JHEP 03
Original
2011
Earlier work this paper cites.
M. Ritzmann, D. A. Kosower and P. Skands, Antenna Showers with Hadronic Initial States , Phys. Lett. B718
Original
2013
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam and Y. LeCun, Spectral networks and locally connected networks on graphs , CoRR abs/1312.6203
Original
2013
Earlier work this paper cites.
K. Cho, B. van Merriënboer, D. Bahdanau and Y. Bengio, On the properties of neural machine translation: Encoder-decoder approaches , arXiv preprint arXiv:1409.1259 (2014)
Original
2014
Earlier work this paper cites.

Similar

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk et al., Learning phrase representations using rnn encoder-decoder for statistical machine translation , arXiv preprint arXiv:1406.1078 (2014)
Original
2014
Cited alongside, same era.
J. Chung, C. Gulcehre, K. Cho and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling , arXiv preprint arXiv:1412.3555 (2014)
Original
2014
Cited alongside, same era.
D. Kingma and J. Ba, Adam: A method for stochastic optimization , arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Cited alongside, same era.
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging , JHEP 02
Original
2015
Cited alongside, same era.

Then

G. Louppe, M. Kagan and K. Cranmer, Learning to Pivot with Adversarial Networks , arXiv preprint arXiv:1611.01046 (2016)
Original
2016
Later among the works it cites.
S. R. Bowman, J. Gauthier, A. Rastogi, R. Gupta, C. D. Manning and C. Potts, A fast unified model for parsing and sentence understanding , arXiv preprint arXiv:1603.06021 (2016)
Original
2016
Later among the works it cites.
D. Yogatama, P. Blunsom, C. Dyer, E. Grefenstette and W. Ling, Learning to compose words into sentences with reinforcement learning , arXiv preprint arXiv:1611.09100 (2016)
Original
2016
Later among the works it cites.
M. Niepert, M. Ahmed and K. Kutzkov, Learning convolutional neural networks for graphs , CoRR abs/1605.05273
Original
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

L. G. Almeida, M. Backović, M. Cliche, S. J. Lee and M. Perelstein, Playing Tag with ANN: Boosted Top Identification with Pattern Recognition , JHEP 07

Original

2015

Cited alongside, same era.

X. Chen, X. Qiu, C. Zhu, S. Wu and X. Huang, Sentence modeling with gated recursive neural network , in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing , pp. 793–798, 2015

2015

Cited alongside, same era.

S. R. Bowman, C. D. Manning and C. Potts, Tree-structured composition in neural networks without tree-structured architectures , arXiv preprint arXiv:1506.04834 (2015)

Original

2015

Cited alongside, same era.

M. Henaff, J. Bruna and Y. LeCun, Deep convolutional networks on graph-structured data , CoRR abs/1506.05163

Original

2015

Cited alongside, same era.

Y. Li, D. Tarlow, M. Brockschmidt and R. S. Zemel, Gated graph sequence neural networks , CoRR abs/1511.05493

Original

2015

Cited alongside, same era.

D. Maclaurin, D. Duvenaud, M. Johnson and R. P. Adams, “Autograd: Reverse-mode differentiation of native Python.” http://github.com/HIPS/autograd , 2015

2015

Cited alongside, same era.

D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban and D. Whiteson, Jet Flavor Classification in High-Energy Physics with Deep Neural Networks , Phys. Rev. D94

Original

2016

Cited alongside, same era.

MIT Press, 2016

I. Goodfellow, Y. Bengio and A. Courville, Deep Learning , ch. 10 · 2016

Cited alongside, same era.

M. Defferrard, X. Bresson and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering , CoRR abs/1606.09375

Original

2016

Later among the works it cites.

T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks , arXiv preprint arXiv:1609.02907 (2016)

Original

2016

Later among the works it cites.

T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks , CoRR abs/1609.02907

Original

2016

Later among the works it cites.

P. T. Komiske, E. M. Metodiev and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination , JHEP 01

Original

2017

Closest in time.

1709.10508

Original

M. Russell, Top quark physics in the Large Hadron Collider era , Ph.D. thesis, Glasgow U., 2017 · 2017

Closest in time.

I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe et al., Neural Message Passing for Jet Physics , in Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017) , 2017, https://dl4physicalsciences.github.io/files/nips_dlps_2017_29.pdf

2017

Closest in time.

E. M. Metodiev, B. Nachman and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics , JHEP 10

Original

2017

Closest in time.

T. Cheng, Recursive Neural Networks in Quark/Gluon Tagging , Comput. Softw. Big Sci. 2

Original

2018

Closest in time.

K. Datta and A. J. Larkoski, Novel Jet Observables from Machine Learning , JHEP 03

Original

2018

Closest in time.

P. T. Komiske, E. M. Metodiev and J. Thaler, Energy flow polynomials: A complete linear basis for jet substructure , JHEP 04

Original

2018

Closest in time.