The HASYv2 dataset

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, Nov. 1998. [Online]. Available: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf

1998

Earlier work this paper cites.

D. Kirsch, “Detexify: Erkennung handgemalter LaTeX-symbole,” Diploma thesis, Westfälische Wilhelms-Universität Münster, 10 2010. [Online]. Available: http://danielkirs.ch/thesis.pdf

2010

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, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011

2011

Earlier work this paper cites.

L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus, “Regularization of neural networks using dropconnect,” in Proceedings of the 30th International Conference on Machine Learning (ICML-13) , 2013, pp. 1058–1066. [Online]. Available: http://www.matthewzeiler.com/pubs/icml2013/icml2013.pdf

2013

Earlier work this paper cites.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , Dec. 2014. [Online]. Available: https://arxiv.org/abs/1412.6980

Original

2014

Cited alongside, same era.

——, “Detexify data,” Jul. 2014. [Online]. Available: https://github.com/kirel/detexify-data

2014

Cited alongside, same era.

M. Thoma, “On-line Recognition of Handwritten Mathematical Symbols,” Bachelor’s Thesis, Karlsruhe Institute of Technology, Karlsruhe, Germany, Nov. 2014. [Online]. Available: http://martin-thoma.com/write-math

2014

Cited alongside, same era.

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, software available from tensorflow.org. [Online]. Available: http://tensorflow.org/

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385 , Dec. 2015. [Online]. Available: https://arxiv.org/pdf/1512.03385v1.pdf

Original

2015

Later among the works it cites.

G. Huang, Z. Liu, and K. Q. Weinberger, “Densely connected convolutional networks,” arXiv preprint arXiv:1608.06993 , Aug. 2016. [Online]. Available: https://arxiv.org/abs/1608.06993v1

Original

2016

Later among the works it cites.

“Deep mnist for experts,” Dec. 2016. [Online]. Available: https://www.tensorflow.org/tutorials/mnist/pros/

2016

Later among the works it cites.

“Deep mnist for experts,” Dec. 2016. [Online]. Available: https://www.tensorflow.org/tutorials/mnist/pros/

2016

Later among the works it cites.