\begin{thebibliography}{10}
\bibitem{thomson2013modern}
Mark Thomson.
\newblock {\em Modern particle physics}.
\newblock Cambridge University Press, 2013.
\bibitem{abe2008precision}
S~Abe, T~Ebihara, S~Enomoto, K~Furuno, Y~Gando, K~Ichimura, H~Ikeda, K~Inoue,
Y~Kibe, Y~Kishimoto, et~al.
\newblock Precision measurement of neutrino oscillation parameters with
kamland.
\newblock {\em Physical Review Letters}, 100(22):221803, 2008.
\bibitem{adamson2011measurement}
P~Adamson, C~Andreopoulos, R~Armstrong, DJ~Auty, DS~Ayres, C~Backhouse, G~Barr,
M~Bishai, A~Blake, GJ~Bock, et~al.
\newblock Measurement of the neutrino mass splitting and flavor mixing by
minos.
\newblock {\em Physical Review Letters}, 106(18):181801, 2011.
\bibitem{blondel2013research}
A~Blondel, A~Bravar, M~Pohl, S~Bachmann, N~Berger, M~Kiehn, A~Sch{\"o}ning,
D~Wiedner, B~Windelband, P~Eckert, et~al.
\newblock Research proposal for an experiment to search for the decay $\mu
\rightarrow eee$.
\newblock {\em arXiv preprint arXiv:1301.6113}, 2013.
\bibitem{augustin2017mupix}
Heiko Augustin, Niklaus Berger, Sebastian Dittmeier, Carsten Grzesik, Jan
Hammerich, Qinhua Huang, Lennart Huth, Moritz Kiehn, Alexandr Kozlinskiy,
Frank~Meier Aeschbacher, et~al.
\newblock The mupix system-on-chip for the mu3e experiment.
\newblock {\em Nuclear Instruments and Methods in Physics Research Section A:
Accelerators, Spectrometers, Detectors and Associated Equipment},
845:194--198, 2017.
\bibitem{philipp2015hv}
Jonathan Philipp, Lennart Huth, Heiko Augustin, Raphael Philipp, Dirk Wiedner,
Niklaus Berger, Mu3e Collaboration, et~al.
\newblock Das hv-maps basierte mupix teleskop.
\newblock Technical report, Detector RD at DESY Test beam, 2015.
\bibitem{augustin2015mupix}
Heiko Augustin, N~Berger, S~Bravar, Simon Corrodi, A~Damyanova, F~F{\"o}rster,
R~Gredig, A~Herkert, Q~Huang, L~Huth, et~al.
\newblock The mupix high voltage monolithic active pixel sensor for the mu3e
experiment.
\newblock {\em Journal of Instrumentation}, 10(03):C03044, 2015.
\bibitem{connor1994recurrent}
Jerome~T Connor, R~Douglas Martin, and Les~E Atlas.
\newblock Recurrent neural networks and robust time series prediction.
\newblock {\em IEEE transactions on neural networks}, 5(2):240--254, 1994.
\bibitem{grossberg2013recurrent}
Stephen Grossberg.
\newblock Recurrent neural networks.
\newblock {\em Scholarpedia}, 8(2):1888, 2013.
\bibitem{ML:XGBoost}
Tianqi Chen and Carlos Guestrin.
\newblock Xgboost: {A} scalable tree boosting system.
\newblock {\em CoRR}, abs/1603.02754, 2016.
\bibitem{chollet2015keras}
Fran{\c{c}}ois Chollet et~al.
\newblock Keras: Deep learning library for theano and tensorflow.
\newblock {\em URL: https://keras. io/k}, 7(8), 2015.
\bibitem{abadi2016tensorflow}
Mart{\'\i}n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey
Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et~al.
\newblock Tensorflow: a system for large-scale machine learning.
\newblock In {\em OSDI}, volume~16, pages 265--283, 2016.
\bibitem{klambauer2017self}
G{\"u}nter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter.
\newblock Self-normalizing neural networks.
\newblock In {\em Advances in Neural Information Processing Systems}, pages
971--980, 2017.
\bibitem{chilimbi2014project}
Trishul~M Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman.
\newblock Project adam: Building an efficient and scalable deep learning
training system.
\newblock In {\em OSDI}, volume~14, pages 571--582, 2014.
\bibitem{ioffe2015batch}
Sergey Ioffe and Christian Szegedy.
\newblock Batch normalization: Accelerating deep network training by reducing
internal covariate shift.
\newblock {\em arXiv preprint arXiv:1502.03167}, 2015.
\bibitem{cooijmans2016recurrent}
Tim Cooijmans, Nicolas Ballas, C{\'e}sar Laurent, {\c{C}}a{\u{g}}lar
G{\"u}l{\c{c}}ehre, and Aaron Courville.
\newblock Recurrent batch normalization.
\newblock {\em arXiv preprint arXiv:1603.09025}, 2016.
\bibitem{schuster1997bidirectional}
Mike Schuster and Kuldip~K Paliwal.
\newblock Bidirectional recurrent neural networks.
\newblock {\em IEEE Transactions on Signal Processing}, 45(11):2673--2681,
1997.
\bibitem{gers1999learning}
Felix~A Gers, J{\"u}rgen Schmidhuber, and Fred Cummins.
\newblock Learning to forget: Continual prediction with lstm.
\newblock 1999.
\bibitem{chung2014empirical}
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio.
\newblock Empirical evaluation of gated recurrent neural networks on sequence
modeling.
\newblock {\em arXiv preprint arXiv:1412.3555}, 2014.
\bibitem{agostinelli2003s}
S~Agostinelli.
\newblock S. agostinelli et al.(geant4 collaboration), nucl. instrum. methods
phys. res., sect. a 506, 250 (2003).
\newblock {\em Nucl. Instrum. Methods Phys. Res., Sect. A}, 506:250, 2003.
\bibitem{pedregosa2011scikit}
Fabian Pedregosa, Ga{\"e}l Varoquaux, Alexandre Gramfort, Vincent Michel,
Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron
Weiss, Vincent Dubourg, et~al.
\newblock Scikit-learn: Machine learning in python.
\newblock {\em Journal of machine learning research}, 12(Oct):2825--2830, 2011.
\bibitem{ML:ROC_AUC:Bradley:1997:UAU:1746432.1746434}
Andrew~P. Bradley.
\newblock The use of the area under the roc curve in the evaluation of machine
learning algorithms.
\newblock {\em Pattern Recogn.}, 30(7):1145--1159, July 1997.
\bibitem{gent1992special}
CR~Gent and CP~Sheppard.
\newblock Special feature. predicting time series by a fully connected neural
network trained by back propagation.
\newblock {\em Computing \& Control Engineering Journal}, 3(3):109--112, 1992.
\bibitem{graves2013speech}
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton.
\newblock Speech recognition with deep recurrent neural networks.
\newblock In {\em Acoustics, speech and signal processing (icassp), 2013 ieee
international conference on}, pages 6645--6649. IEEE, 2013.
\end{thebibliography}
