Encouraging an appropriate representation simplifies training of neural networks

[1] E. Ackerman, Slight street sign modifications can completely fool machine learning algorithms, IEEE Spectrum 6 (2019). ⇒103Search in Google Scholar

[2] D. Amodei, S. Ananthanarayanan, R. Anubhai, J. Bai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, Q. Cheng, G. Chen, et al., Deep speech 2: end-to-end speech recognition in english and mandarin, in: International conference on machine learning, 2016, pp. 173–182. ⇒103Search in Google Scholar

[3] M. Antal, E. Egyed-Zsigmond, Intrusion detection using mouse dynamics IET Biometrics 8 (5) (2019) 285–294. ⇒109Search in Google Scholar

[4] M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al., End to end learning for self-driving cars, arXiv (2016) arXiv:1604.07316. ⇒103Search in Google Scholar

[5] A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks, Nature 542 (2017) 115. ⇒103Search in Google Scholar

[6] J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, et al., Clinically applicable deep learning for diagnosis and referral in retinal disease, Nature medicine 24 (2018) 1342. ⇒103Search in Google Scholar

[7] A. Gordo, J. Almazan, J. Revaud, D. Larlus, End-to-end learning of deep visual representations for image retrieval, International Journal of Computer Vision 124 (2017) 237–254. ⇒10310.1007/s11263-017-1016-8Search in Google Scholar

[8] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv (2014) arXiv:1412.6980. ⇒106Search in Google Scholar

[9] R. J. Meszlényi, K. Buza, Z. Vidnyánszky, Resting state fmri functional connectivity-based classification using a convolutional neural network architecture, Frontiers in neuroinformatics 11 (2017) 61. ⇒103Search in Google Scholar

[10] K. Miok, D. Nguyen-Doan, M. Robnik-Sikonja, D. Zaharie, Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders, in: E-Health and Bioengineering Conference (EHB) (2019) ⇒10910.1109/EHB47216.2019.8969940Search in Google Scholar

[11] T. Nyíri, A. Kiss, Novel Ensembling Methods for Dermatological Image Classification, in: International Conference on Theory and Practice of Natural Computing (2018) ⇒10910.1007/978-3-030-04070-3_34Search in Google Scholar

[12] N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, A. Swami, Practical black-box attacks against machine learning, in: Proceedings of the 2017 ACM on Asia conference on computer and communications security, ACM, 2017, pp. 506–519. ⇒10310.1145/3052973.3053009Search in Google Scholar

[13] L. Peška, K. Buza, J. Koller, Drug-target interaction prediction: A bayesian ranking approach, Computer methods and programs in biomedicine 152 (2017) 15–21. ⇒10410.1016/j.cmpb.2017.09.00329054256Search in Google Scholar

[14] D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez, T. Harley, G. Dulac-Arnold, D. Reichert, N. Rabinowitz, A. Barreto, et al., The predictron: end-toend learning and planning, in: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, 2017, pp. 3191–3199. ⇒103Search in Google Scholar

[15] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al., Mastering the game of go with deep neural networks and tree search, Nature 529 (2016) 484. ⇒103Search in Google Scholar

[16] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks, arXiv (2013) arXiv:1312.6199. ⇒103Search in Google Scholar

[17] S. M. Szilagyi, L. Szilagyi, D. Iclanzan, Z. Benyó, Unified Neural Network Based Adaptive ECG Signal Analysis and Compression, Scientific Bulletin of the Politechnica University of Timisoara, Transactions on Automatic Control and Computer Science 51.65 (2006) 27–36. ⇒109Search in Google Scholar

[18] J. Zhang, C. Zong, et al., Deep neural networks in machine translation: An overview, IEEE Intelligent Systems 30 (2015) 16–25. ⇒10410.1109/MIS.2015.69Search in Google Scholar