ML Anthology

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

S. Indrapriyadarsini, Hiroshi Ninomiya, Takeshi Kamio, Hideki Asai
AAAI 2022 pp. 12884-12885
doi:10.1609/AAAI.V36I11.21579 /aaai/2022/indrapriyadarsini2022aaai-practical/

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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Text

Indrapriyadarsini et al. "On the Practical Robustness of the Nesterov's Accelerated Quasi-Newton Method." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21579

Markdown

[Indrapriyadarsini et al. "On the Practical Robustness of the Nesterov's Accelerated Quasi-Newton Method." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/indrapriyadarsini2022aaai-practical/) doi:10.1609/AAAI.V36I11.21579

BibTeX

@inproceedings{indrapriyadarsini2022aaai-practical,
  title     = {{On the Practical Robustness of the Nesterov's Accelerated Quasi-Newton Method}},
  author    = {Indrapriyadarsini, S. and Ninomiya, Hiroshi and Kamio, Takeshi and Asai, Hideki},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12884-12885},
  doi       = {10.1609/AAAI.V36I11.21579},
  url       = {https://mlanthology.org/aaai/2022/indrapriyadarsini2022aaai-practical/}
}