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A. Alloul, N. D. Christensen, C. Degrande, C. Duhr, and B. Fuks, “FeynRules 2.0 - A complete toolbox for tree-level phenomenology,” Comput. Phys. Commun
Original
1921
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems
1989
Earlier work this paper cites.
Academic Press, Boston, second edition ed., 1990
K. Fukunaga, Introduction to Statistical Pattern Recognition (Second Edition) · 1990
Earlier work this paper cites.
Institute of Electrical and Electronics Engineers Inc., United States, 1996
K. Tumer and J. Ghosh, Estimating the Bayes error rate through classifier combining · 1996
Earlier work this paper cites.
A. Leike, “The Phenomenology of extra neutral gauge bosons,” Phys. Rept
1999
Earlier work this paper cites.
F. Pedregosa et al
2011
Earlier work this paper cites.
J. Stevens and M. Williams, “uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers,” JINST
Original
2013
Earlier work this paper cites.
P. Baldi, P. Sadowski, and D. Whiteson, “Searching for Exotic Particles in High-Energy Physics with Deep Learning,” Nature Commun
Original
2014
Earlier work this paper cites.

Similar

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR
Original
2014
Cited alongside, same era.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP
Original
2014
Cited alongside, same era.
P. Baldi, P. Sadowski, and D. Whiteson, “Enhanced Higgs Boson to τ + τ − \tau^{+}\tau^{-} Search with Deep Learning,” Phys. Rev. Lett
Original
2015
Cited alongside, same era.
A. Rogozhnikov, A. Bukva, V. V. Gligorov, A. Ustyuzhanin, and M. Williams, “New approaches for boosting to uniformity,” JINST
Original
2015
Cited alongside, same era.

Then

P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, “Jet Substructure Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev
Original
2016
Later among the works it cites.
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
Original
2016
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images � deep learning edition,” JHEP
Original
2016
Later among the works it cites.
J. Dolen, P. Harris, S. Marzani, S. Rappoccio, and N. Tran, “Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure,” JHEP
Original
2016
Later among the works it cites.
G. Kasieczka, T. Plehn, M. Russell, and T. Schell, “Deep-learning Top Taggers or The End of QCD?,” JHEP

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Reading the bibliography…

2017

What is the Machine Learning?

Chang, Spencer, Cohen, Timothy, Ostdiek, Bryan

Understand

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency.

To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background.
Weights are introduced to smooth away the features in a given variable(s).
New networks are then trained on this modified data.

Built on

A. Alloul, N. D. Christensen, C. Degrande, C. Duhr, and B. Fuks, “FeynRules 2.0 - A complete toolbox for tree-level phenomenology,” Comput. Phys. Commun
Original
1921
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems
1989
Earlier work this paper cites.
Academic Press, Boston, second edition ed., 1990
K. Fukunaga, Introduction to Statistical Pattern Recognition (Second Edition) · 1990
Earlier work this paper cites.
Institute of Electrical and Electronics Engineers Inc., United States, 1996
K. Tumer and J. Ghosh, Estimating the Bayes error rate through classifier combining · 1996
Earlier work this paper cites.
A. Leike, “The Phenomenology of extra neutral gauge bosons,” Phys. Rept
1999
Earlier work this paper cites.
F. Pedregosa et al
2011
Earlier work this paper cites.
J. Stevens and M. Williams, “uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers,” JINST
Original
2013
Earlier work this paper cites.
P. Baldi, P. Sadowski, and D. Whiteson, “Searching for Exotic Particles in High-Energy Physics with Deep Learning,” Nature Commun
Original
2014
Earlier work this paper cites.

Similar

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR
Original
2014
Cited alongside, same era.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP
Original
2014
Cited alongside, same era.
P. Baldi, P. Sadowski, and D. Whiteson, “Enhanced Higgs Boson to τ + τ − \tau^{+}\tau^{-} Search with Deep Learning,” Phys. Rev. Lett
Original
2015
Cited alongside, same era.
A. Rogozhnikov, A. Bukva, V. V. Gligorov, A. Ustyuzhanin, and M. Williams, “New approaches for boosting to uniformity,” JINST
Original
2015
Cited alongside, same era.

Then

P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, “Jet Substructure Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev
Original
2016
Later among the works it cites.
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
Original
2016
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images � deep learning edition,” JHEP
Original
2016
Later among the works it cites.
J. Dolen, P. Harris, S. Marzani, S. Rappoccio, and N. Tran, “Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure,” JHEP
Original
2016
Later among the works it cites.
G. Kasieczka, T. Plehn, M. Russell, and T. Schell, “Deep-learning Top Taggers or The End of QCD?,” JHEP

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. Backovic, M. Cliche, S. J. Lee, and M. Perelstein, “Playing Tag with ANN: Boosted Top Identification with Pattern Recognition,” JHEP

Original

2015

Cited alongside, same era.

F. Chollet et al

2015

Cited alongside, same era.

http://tensorflow.org/ . Software available from tensorflow.org

M. Abadi et al · 2015

Cited alongside, same era.

J. A. Aguilar-Saavedra, J. H. Collins, and R. K. Mishra, “A generic anti-QCD jet tagger,” arXiv:1709.01087 [hep-ph]

Original

Cited in the paper.