Skip to main content
SpringerLink
Log in

Machine learning after the deep learning revolution

  • Perspective
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  3. Buntine W L. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 1994, 1: 159–225

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, 3800, Australia

    Wray Buntine

Authors
  1. Wray Buntine
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wray Buntine.

Additional information

Wray Buntine is a professor at Monash University where he founded the Master of Data Science, and is director of the Machine Learning Group. He was previously at NICTA in Canberra, Helsinki Institute for Information Technology, NASA Ames Research Center, UC Berkeley, and Google, as well as various startups. He is known for his theoretical and applied work and in probabilistic methods for document and text analysis, social networks, data mining and machine learning. He is on several journal editorial boards and has been senior programme committee member for conferences such as IJCAI, UAI, ACML and SIGKDD. He has over 200 academic publications, several software products and two patents.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buntine, W. Machine learning after the deep learning revolution. Front. Comput. Sci. 14, 146320 (2020). https://doi.org/10.1007/s11704-020-0800-8

Download citation

  • Received:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-020-0800-8

Search

Navigation