The Quest for Mathematical Understanding of Deep Learning (Invited Talk)

Author Sanjeev Arora

Thumbnail PDF


  • Filesize: 175 kB
  • 1 pages

Document Identifiers

Author Details

Sanjeev Arora
  • Computer Science Department, Princeton University, NJ, USA

Cite AsGet BibTex

Sanjeev Arora. The Quest for Mathematical Understanding of Deep Learning (Invited Talk). In 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 182, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


Deep learning has transformed Machine Learning and Artificial Intelligence in the past decade. It raises fundamental questions for mathematics and theory of computer science, since it relies upon solving large-scale nonconvex problems via gradient descent and its variants. This talk will be an introduction to mathematical questions raised by deep learning, and some partial understanding obtained in recent years.

Subject Classification

ACM Subject Classification
  • Theory of computation → Mathematical optimization
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Machine learning
  • machine learning
  • artificial intelligence
  • deep learning
  • gradient descent
  • optimization


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail