B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey, Predicting the sequence specificities of dna-and rna-binding proteins by deep learning, Nature biotechnology, vol.33, issue.8, p.831, 2015.

S. F. Altschul, T. L. Madden, A. A. Schäffer, J. Zhang, Z. Zhang et al., Gapped blast and psi-blast: a new generation of protein database search programs, Nucleic acids research, vol.25, issue.17, pp.3389-3402, 1997.

M. Arbel, D. J. Sutherland, M. Bi´nkowskibi´nkowski, and A. Gretton, On gradient regularizers for MMD GANs, Advances in Neural Information Processing Systems (NeurIPS), 2018.

M. Arjovsky, S. Chintala, L. Bottou, . Wasserstein, and . Gan, Proceedings of the International Conference on Machine Learning (ICML), 2017.

P. L. Bartlett, D. J. Foster, and M. J. Telgarsky, Spectrallynormalized margin bounds for neural networks, Advances in Neural Information Processing Systems (NIPS), 2017.

M. Belkin, D. Hsu, M. , and P. , Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate, Advances in Neural Information Processing Systems (NeurIPS), 2018.

M. Belkin, S. Ma, and S. Mandal, To understand deep learning we need to understand kernel learning, Proceedings of the International Conference on Machine Learning (ICML), 2018.

A. Bietti and J. Mairal, Group invariance, stability to deformations, and complexity of deep convolutional representations, Journal of Machine Learning Research, vol.20, issue.25, pp.1-49, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01536004

B. Biggio and F. Roli, Wild patterns: Ten years after the rise of adversarial machine learning, Pattern Recognition, vol.84, pp.317-331, 2018.

M. Bi´nkowskibi´nkowski, D. J. Sutherland, M. Arbel, A. Gretton, . Demystifying et al., Proceedings of the International Conference on Learning Representations (ICLR), 2018.

S. Boucheron, O. Bousquet, and G. Lugosi, Theory of classification: A survey of some recent advances, ESAIM: probability and statistics, vol.9, pp.323-375, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00017923

T. Ching, Opportunities and obstacles for deep learning in biology and medicine, Journal of The Royal Society Interface, vol.15, issue.141, 2018.

M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier, Parseval networks: Improving robustness to adversarial examples, Proceedings of the International Conference on Machine Learning (ICML), 2017.

H. Drucker and Y. Le-cun, Double backpropagation increasing generalization performance, International Joint Conference on Neural Networks (IJCNN), 1991.

G. K. Dziugaite, D. M. Roy, and Z. Ghahramani, Training generative neural networks via maximum mean discrepancy optimization, Conference on Uncertainty in Artificial Intelligence (UAI), 2015.

L. Engstrom, D. Tsipras, L. Schmidt, and A. Madry, A rotation and a translation suffice: Fooling cnns with simple transformations, 2017.

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, A kernel two-sample test, Journal of Machine Learning Research, vol.13, pp.723-773, 2012.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, Improved training of Wasserstein GANs, Advances in Neural Information Processing Systems (NIPS), 2017.

T. Håndstad, A. J. Hestnes, and P. Saetrom, Motif kernel generated by genetic programming improves remote homology and fold detection, BMC bioinformatics, vol.8, issue.1, p.23, 2007.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

S. M. Kakade, K. Sridharan, and A. Tewari, On the complexity of linear prediction: Risk bounds, margin bounds, and regularization, Advances in Neural Information Processing Systems (NIPS), 2009.

J. Khim and P. Loh, Adversarial risk bounds via function transformation, 2018.

V. Koltchinskii and D. Panchenko, Empirical margin distributions and bounding the generalization error of combined classifiers. The Annals of Statistics, vol.30, pp.1-50, 2002.

C. Li, W. Chang, Y. Cheng, Y. Yang, P. et al., Mmd gan: Towards deeper understanding of moment matching network, Advances in Neural Information Processing Systems (NIPS), 2017.