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@Misc{tensorflow2015-whitepaper,
  Title                    = { {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},

  Author                   = {
 Mart\'{\i}n~Abadi and
 Ashish~Agarwal and
 Paul~Barham and
 Eugene~Brevdo and
 Zhifeng~Chen and
 Craig~Citro and
 Greg~S.~Corrado and
 Andy~Davis and
 Jeffrey~Dean and
 Matthieu~Devin and
 Sanjay~Ghemawat and
 Ian~Goodfellow and
 Andrew~Harp and
 Geoffrey~Irving and
 Michael~Isard and
 Yangqing Jia and
 Rafal~Jozefowicz and
 Lukasz~Kaiser and
 Manjunath~Kudlur and
 Josh~Levenberg and
 Dan~Man\'{e} and
 Rajat~Monga and
 Sherry~Moore and
 Derek~Murray and
 Chris~Olah and
 Mike~Schuster and
 Jonathon~Shlens and
 Benoit~Steiner and
 Ilya~Sutskever and
 Kunal~Talwar and
 Paul~Tucker and
 Vincent~Vanhoucke and
 Vijay~Vasudevan and
 Fernanda~Vi\'{e}gas and
 Oriol~Vinyals and
 Pete~Warden and
 Martin~Wattenberg and
 Martin~Wicke and
 Yuan~Yu and
 Xiaoqiang~Zheng},
  Note                     = {Software available from tensorflow.org},
  Year                     = {2015},

  Url                      = {http://tensorflow.org/}
}

@Article{deep-residual-networks-2015,
  Title                    = {Deep residual learning for image recognition},
  Author                   = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  Journal                  = {arXiv preprint arXiv:1512.03385},
  Year                     = {2015},

  Month                    = dec,

  Url                      = {https://arxiv.org/pdf/1512.03385v1.pdf}
}

@Article{huang2016densely,
  Title                    = {Densely connected convolutional networks},
  Author                   = {Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q},
  Journal                  = {arXiv preprint arXiv:1608.06993},
  Year                     = {2016},

  Month                    = aug,

  Url                      = {https://arxiv.org/abs/1608.06993v1}
}

@Article{kingma2014adam,
  Title                    = {Adam: A method for stochastic optimization},
  Author                   = {Kingma, Diederik and Ba, Jimmy},
  Journal                  = {arXiv preprint arXiv:1412.6980},
  Year                     = {2014},

  Month                    = dec,

  Url                      = {https://arxiv.org/abs/1412.6980}
}

@Misc{Kirsch2014,
  Title                    = {Detexify data},

  Author                   = {Daniel Kirsch},
  Month                    = jul,
  Year                     = {2014},

  Url                      = {https://github.com/kirel/detexify-data}
}

@MastersThesis{Kirsch,
  Title                    = {Detexify: Erkennung handgemalter {L}a{T}e{X}-Symbole},
  Author                   = {Daniel Kirsch},
  School                   = {Westfälische Wilhelms-Universität Münster},
  Year                     = {2010},
  Month                    = {10},
  Type                     = {Diploma thesis},

  Url                      = {http://danielkirs.ch/thesis.pdf}
}

@Article{LeNet-5,
  Title                    = {Gradient-based learning applied to document recognition},
  Author                   = {LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  Journal                  = {Proceedings of the IEEE},
  Year                     = {1998},

  Month                    = nov,
  Number                   = {11},
  Pages                    = {2278-2324},
  Volume                   = {86},

  Doi                      = {10.1109/5.726791},
  ISSN                     = {0018-9219},
  Keywords                 = {backpropagation;convolution;multilayer perceptrons;optical character recognition;2D shape variability;GTN;back-propagation;cheque reading;complex decision surface synthesis;convolutional neural network character recognizers;document recognition;document recognition systems;field extraction;gradient based learning technique;gradient-based learning;graph transformer networks;handwritten character recognition;handwritten digit recognition task;high-dimensional patterns;language modeling;multilayer neural networks;multimodule systems;performance measure minimization;segmentation recognition;Character recognition;Feature extraction;Hidden Markov models;Machine learning;Multi-layer neural network;Neural networks;Optical character recognition software;Optical computing;Pattern recognition;Principal component analysis},
  Url                      = {http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf}
}

@Article{scikit-learn,
  Title                    = {Scikit-learn: Machine Learning in {P}ython},
  Author                   = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
 and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
 and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
 Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  Journal                  = {Journal of Machine Learning Research},
  Year                     = {2011},
  Pages                    = {2825--2830},
  Volume                   = {12}
}

@InProceedings{risi2010evolving,
  Title                    = {Evolving the placement and density of neurons in the hyperneat substrate},
  Author                   = {Risi, Sebastian and Lehman, Joel and Stanley, Kenneth O},
  Booktitle                = {Proceedings of the 12th annual conference on Genetic and evolutionary computation},
  Year                     = {2010},
  Organization             = {ACM},
  Pages                    = {563--570}
}

@Article{salzberg1997comparing,
  Title                    = {On comparing classifiers: Pitfalls to avoid and a recommended approach},
  Author                   = {Salzberg, Steven L},
  Journal                  = {Data mining and knowledge discovery},
  Year                     = {1997},
  Number                   = {3},
  Pages                    = {317--328},
  Volume                   = {1},

  Publisher                = {Springer}
}

@MastersThesis{Thoma:2014,
  Title                    = {On-line {Recognition} of {Handwritten} {Mathematical} {Symbols}},
  Author                   = {Martin Thoma},
  School                   = {Karlsruhe Institute of Technology},
  Year                     = {2014},

  Address                  = {Karlsruhe, Germany},
  Month                    = nov,
  Type                     = {Bachelor’s Thesis},

  Keywords                 = {handwriting recognition; on-line; machine learning;
artificial neural networks; mathematics; classification;
supervised learning; MLP; multilayer perceptrons; hwrt;
write-math},
  Url                      = {http://martin-thoma.com/write-math}
}

@InProceedings{wan2013regularization,
  Title                    = {Regularization of neural networks using dropconnect},
  Author                   = {Wan, Li and Zeiler, Matthew and Zhang, Sixin and Cun, Yann L and Fergus, Rob},
  Booktitle                = {Proceedings of the 30th International Conference on Machine Learning (ICML-13)},
  Year                     = {2013},
  Pages                    = {1058--1066},

  Url                      = {http://www.matthewzeiler.com/pubs/icml2013/icml2013.pdf}
}

@Misc{tf-mnist,
  Title                    = {Deep MNIST for Experts},
  Month                    = dec,
  Year                     = {2016},

  Url                      = {https://www.tensorflow.org/tutorials/mnist/pros/}
}

@Misc{TF-MNIST-2016,
  Title                    = {Deep MNIST for Experts},
  Month                    = dec,
  Year                     = {2016},

  Url                      = {https://www.tensorflow.org/tutorials/mnist/pros/}
}