On the Practical Robustness of the Nesterov’s Accelerated Quasi-Newton Method

Authors

  • S. Indrapriyadarsini Shizuoka University, Japan
  • Hiroshi Ninomiya Shonan Institute of Technology, Japan
  • Takeshi Kamio Hiroshima City University, Japan
  • Hideki Asai Shizuoka University, Japan

DOI:

https://doi.org/10.1609/aaai.v36i11.21579

Keywords:

Optimization Algorithm, Quasi-Newton Method, Neural Networks, Second-order, Nesterov’s Accelerated Gradient

Abstract

This study focuses on the Nesterov's accelerated quasi-Newton (NAQ) method in the context of deep neural networks (DNN) and its applications. The thesis objective is to confirm the robustness and efficiency of Nesterov's acceleration to quasi-Netwon (QN) methods by developing practical algorithms for different fields of optimization problems.
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Published

2022-06-28

How to Cite

Indrapriyadarsini, S., Ninomiya, H., Kamio, T., & Asai, H. (2022). On the Practical Robustness of the Nesterov’s Accelerated Quasi-Newton Method. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12884-12885. https://doi.org/10.1609/aaai.v36i11.21579

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

The Twenty - Seventh AAAI / SIGAI Doctoral Consortium