Fetching the paper…
Reading the bibliography…
This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs.
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, L. Jackel, Handwritten digit recognition with a back-propagation network, Advances in neural information processing systems 2 (1989) 396–404
1989
Earlier work this paper cites.
R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization 11 (4) (1997) 341–359
1997
Earlier work this paper cites.
D. Heck, J. Knapp, J. Capdevielle, G. Schatz, T. Thouw, A monte carlo code to simulate extensive air showers, Report FZKA 6019 (1998)
1998
Earlier work this paper cites.
L. Breiman, Random forests, Machine learning 45 (1) (2001) 5–32
2001
Earlier work this paper cites.
J. H. Friedman, Greedy function approximation: a gradient boosting machine, Annals of statistics (2001) 1189–1232
2001
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, Smote: Synthetic minority over-sampling technique, J. Artif. Int. Res. 16 (1) (2002) 321–357
2002
Earlier work this paper cites.
S. Agostinelli, J. Allison, K. a. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. . Barrand, et al., Geant4—a simulation toolkit, Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment 506 (3) (2003) 250–303
2003
Earlier work this paper cites.
IEEE Transactions on Nuclear Science 53 No. 1 (2006) 270–278
2006
Earlier work this paper cites.
doi:10.1109/MCSE.2007.58
T. E. Oliphant, Python for scientific computing, Computing in Science Engineering 9 (3) (2007) 10–20 · 2007
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830
2011
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Cited alongside, same era.
F. Chollet, et al., Keras, https://github.com/fchollet/keras (2015)
2015
Cited alongside, same era.
Nuclear Instruments and Methods in Physics Research A 835 (2016) 186–225
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016, http://www.deeplearningbook.org
2016
Cited alongside, same era.
T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794
2016
Cited alongside, same era.
A. Guillén, C. Todero, J. C. Martínez, L. J. Herrera, A preliminary approach to composition classification of ultra-high energy cosmic rays, in: International Conference on Applied Physics, System Science and Computers, Springer, 2018, pp. 196–202
2018
Later among the works it cites.
doi:https://doi.org/10.1016/j.astropartphys.2019.03.001
A. Guillén, A. Bueno, J. Carceller, J. Martínez-Velázquez, G. Rubio, C. T. Peixoto, P. Sanchez-Lucas, Deep learning techniques applied to the physics of extensive air showers , Astroparticle Physics 111 (2019) 12 – 22 · 2019
Later among the works it cites.
2019
Later among the works it cites.
F. Carrillo-Perez, L. J. Herrera, J. M. Carceller, A. Guillén, Improving classification of ultra-high energy cosmic rays using spacial locality by means of a convolutional dnn, in: International Work-Conference on Artificial Neural Networks, Springer, 2019, pp. 222–232
2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
doi:10.1007/978-3-319-59153-7\_12
M. Manzano, A. Guillén, I. Rojas, L. J. Herrera, Deep learning using EEG data in time and frequency domains for sleep stage classification , in: I. Rojas, G. Joya, A. Català (Eds.), Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Cadiz, Spain, June 14-16, 2017, Proceedings, Part I, Vol. 10305 of Lecture Notes in Computer Science, Springer, 2017, pp. 132–141 · 2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Barber, D. Kieda, W. Springer, H. Collaboration, et al., Detection of near horizontal muons with the hawc observatory, in: ICRC, Vol. 301, 2017, p. 512
2017
Cited alongside, same era.
M. Manzano, A. Guillén, I. Rojas, L. J. Herrera, Combination of eeg data time and frequency representations in deep networks for sleep stage classification, in: International Conference on Intelligent Computing, Springer, 2017, pp. 219–229
2017
Cited alongside, same era.
N. Choma, F. Monti, L. Gerhardt, T. Palczewski, Z. Ronaghi, P. Prabhat, W. Bhimji, M. Bronstein, S. Klein, J. Bruna, Graph neural networks for icecube signal classification, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2018, pp. 386–391
2018
Cited alongside, same era.
A. De Angelis, M. Pimenta, Introduction to particle and astroparticle physics: multimessenger astronomy and its particle physics foundations, Springer, 2018
2018
Cited alongside, same era.
P. Assis, R. Conceição, M. Pimenta, B. Tomé, A. Blanco, P. Fonte, L. Lopes, U. B. de Almeida, R. Shellard, B. D. Piazzoli, et al., Lattes: a novel detector concept for a gamma-ray experiment in the southern hemisphere
Cited in the paper.
Later among the works it cites.
2019
Later among the works it cites.
doi:https://doi.org/10.1016/j.ymssp.2020.107398
S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, D. J. Inman, 1d convolutional neural networks and applications: A survey , Mechanical Systems and Signal Processing 151 (2021) 107398 · 2020
Later among the works it cites.
doi:https://doi.org/10.1016/j.inffus.2020.10.001
L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, A. Liotta, Smart anomaly detection in sensor systems: A multi-perspective review , Information Fusion 67 (2021) 64 – 79 · 2020
Later among the works it cites.
doi:10.1016/j.eswa.2020.113911
A. Peimankar, S. Puthusserypady, Dens-ecg: A deep learning approach for ecg signal delineation , Expert Systems with Applications 165, cited By 0 (2021) · 2020
Later among the works it cites.
doi:10.3390/e22111216
A. Guillén, J. Martínez, J. M. Carceller, L. J. Herrera, A comparative analysis of machine learning techniques for muon count in uhecr extensive air-showers , Entropy 22 (11) (2020) · 2020
Later among the works it cites.
doi:10.1088/1742-6596/1603/1/012024
B. S. González, R. Conceição, B. Tomé, M. Pimenta, L. J. Herrera, A. Guillen, Using convolutional neural networks for muon detection in WCD tank , Journal of Physics: Conference Series 1603 (2020) 012024 · 2020
Later among the works it cites.
xgboost developers, XGBoost Python Package, https://xgboost.readthedocs.io/en/latest/python/index.html (2020)
2020
Later among the works it cites.