Machine learning after the deep learning revolution

References

[1] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521( 7553): 436–444

[2] Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798–1828

[3] Buntine W L. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 1994, 2: 159–225

[4] Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019

[5] Donoho D. 50 years of data science. Journal of Computational and Graphical Statistics, 2017, 26(4): 745–766

[6] Hutter F, Kotthoff L, Vanschoren J. Automated Machine Learning: Methods, Systems, Challenges. Springer, 2018

[7] van de Meent J W, Paige B, Yang H, Wood F. An introduction to probabilistic programming. 2018, arXiv preprint arXiv:1809.10756

[8] Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning requires rethinking generalization. In: Proceedings of the 5th International Conference on Learning Representations. 2017

[9] Bengio Y. From system 1 deep learning to system 2 deep learning. In: Proceedings of NeurIPS. 2019

RIGHTS & PERMISSIONS

2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature