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A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques.
A. Butter et al., The Machine Learning Landscape of Top Taggers
1902
Earlier work this paper cites.
1904
Earlier work this paper cites.
S. J. Reddi, S. Kale, and S. Kumar, On the Convergence of Adam and Beyond · 1904
Earlier work this paper cites.
1908
Earlier work this paper cites.
1909
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k T k_{\mathrm{T}} jet clustering algorithm
2008
Earlier work this paper cites.
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K. Muller, How to explain individual classification decisions
2010
Earlier work this paper cites.
G. P. Salam, Towards Jetography
2010
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure
2010
Earlier work this paper cites.
https://dl.acm.org/doi/10.5555/3104322.3104425
V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines · 2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
A. Abdesselam et al., Boosted Objects: A Probe of Beyond the Standard Model Physics
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
2011
Earlier work this paper cites.
http://proceedings.mlr.press/v15/glorot11a.html
X. Glorot, A. Bordes, and Y. Bengio, Deep Sparse Rectifier Neural Networks · 2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
2012
Earlier work this paper cites.
M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, Towards an understanding of jet substructure
2013
Cited alongside, same era.
2014
Cited alongside, same era.
A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, Soft Drop
2014
Cited alongside, same era.
D. Kingma and J. Ba, Adam: A Method for Stochastic Optimization
2014
Cited alongside, same era.
D. Adams et al., Towards an Understanding of the Correlations in Jet Substructure
2017
Later among the works it cites.
Z. C. Lipton, The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability is Both Important and Slippery
2018
Later among the works it cites.
2018
Later among the works it cites.
Particle Data Group
2018
Later among the works it cites.
R. Kogler et al., Jet Substructure at the Large Hadron Collider: Experimental Review
2019
Later among the works it cites.
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2015
Cited alongside, same era.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. MÃŒller, and W. Samek, On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Software available from tensorflow.org
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems · 2015
Cited alongside, same era.
2016
Cited alongside, same era.
MIT Press, 2016
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning · 2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, and C. Guestrin, "Why Should I Trust You?": Explaining the Predictions of Any Classifier · 2016
Cited alongside, same era.
W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, Definitions, methods, and applications in interpretable machine learning
2019
Later among the works it cites.
A. Björklund, A. Henelius, E. Oikarinen, K. Kallonen, and K. Puolamäki, Sparse Robust Regression for Explaining Classifiers
2019
Later among the works it cites.
Springer International Publishing, Cham, 2019
G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K.-R. Müller, Layer-Wise Relevance Propagation: An Overview · 2019
Later among the works it cites.
M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K.-R. Müller, S. Dähne, and P.-J. Kindermans, iNNvestigate Neural Networks!
2019
Later among the works it cites.
R. Brun, F. Rademakers, P. Canal, A. Naumann, O. Couet, L. Moneta, V. Vassilev, S. Linev, D. Piparo, G. GANIS, B. Bellenot, E. Guiraud, G. Amadio, wverkerke, P. Mato, TimurP, M. Tadel, wlav, E. Tejedor, J. Blomer, A. Gheata, S. Hageboeck, S. Roiser, marsupial, S. Wunsch, O. Shadura, A. Bose, CristinaCristescu, X. Valls, and R. Isemann, root-project/root: v6.18/02
2019
Later among the works it cites.
J. Pivarski, C. Escott, M. Hedges, N. Smith, C. Escott, J. Rembser, J. Nandi, B. Fischer, H. Schreiner, P. Das, P. Fackeldey, Nollde, and B. Krikler, scikit-hep/awkward-array: 0.12.0rc1
2019
Later among the works it cites.
2020
Closest in time.
K.-F. Chen and Y.-T. Chien, Deep learning jet substructure from two-particle correlations
2020
Closest in time.
G. Kasieczka, S. Marzani, G. Soyez, and G. Stagnitto, Towards machine learning analytics for jet substructure
2020
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V. Mikuni and F. Canelli, ABCNet: an attention-based method for particle tagging
2020
Closest in time.
L. Gray, N. Smith, A. Novak, D. Taylor, P. Fackeldey, C. Carballo, P. Gessinger, J. Pata, A. Woodard, Andreas, B. Fischer, Z. Surma, A. Perloff, D. Noonan, L. Heinrich, N. Amin, P. Das, I. Dutta, J. Duarte, J. Rübenach, and A. R. Hall, Coffeateam/coffea: Release v0.6.46
2020
Closest in time.
J. Pivarski, P. Das, C. Burr, D. Smirnov, M. Feickert, T. Gal, L. Kreczko, N. Smith, N. Biederbeck, O. Shadura, M. Proffitt, benkrikler, H. Dembinski, H. Schreiner, J. Rembser, M. R., C. Gu, J. Rübenach, M. Peresano, and R. Turra, scikit-hep/uproot: 3.12.0
2020
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T. Faucett, J. Thaler, and D. Whiteson, Mapping machine-learned physics into a human-readable space
2021
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