Towards solving neural networks with optimization trajectory search

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抜粋

Modern gradient based optimization methods for deep neural networks demonstrate outstanding results on image classification tasks. However, methods that do not rely on gradient feedback fail to tackle deep network optimization. In the field of evolutionary computation, applying evolutionary algorithms directly to network weights remains to be an unresolved challenge. In this paper we examine a new framework for the evolution of deep nets. Based on the empirical analysis, we propose the use of linear sub-spaces of problems to search for promising optimization trajectories in parameter space, opposed to weight evolution. We show that linear sub-spaces of loss functions are sufficiently well-behaved to allow trajectory evaluation. Furthermore, we introduce fitness measure to show that it is possible to correctly categorize trajectories according to their distance from the optimal path. As such, this work introduces an alternative approach to evolutionary optimization of deep networks.

元の言語英語
ホスト出版物のタイトルGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
出版者Association for Computing Machinery, Inc
ページ75-76
ページ数2
ISBN(電子版)9781450367486
DOI
出版物ステータス出版済み - 7 13 2019
イベント2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, チェコ共和国
継続期間: 7 13 20197 17 2019

出版物シリーズ

名前GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

会議

会議2019 Genetic and Evolutionary Computation Conference, GECCO 2019
チェコ共和国
Prague
期間7/13/197/17/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software

フィンガープリント Towards solving neural networks with optimization trajectory search' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Parsenadze, L. T., Vargas, D. V., & Fujita, T. (2019). Towards solving neural networks with optimization trajectory search. : GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 75-76). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3326796