Structural synthesis of fast two-layer neural networks

被引：1
|
作者：
Dorogov, AY [1 ]
机构：
[1] St Petersburg State Electrotech Univ, St Petersburg, Russia
来源：
CYBERNETICS AND SYSTEMS ANALYSIS | 2000年 / 36卷 / 04期
关键词：
neural networks; two-layer neural networks; fast neural networks (FNNs); dense neural networks; one-rank networks; fast two-layer neural networks; number of degrees of freedom of neural networks; plasticity (trainability) of neural networks; structural synthesis of fast two-layer neural networks;
D O I：
10.1007/BF02667059
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Methods of construction of structural models of fast two-layer neural networks are considered. The methods are based on the criteria of minimum computing operations and maximum degrees of freedom. Optimal structural models of two-layer neural networks are constructed. Illustrative examples are given.
引用
收藏
页码：512 / 519
页数：8
相关论文
共 50 条
  • [41] Self-Regularity of Output Weights for Overparameterized Two-Layer Neural Networks
    Gamarnik, David
    Kizildag, Eren C.
    Zadik, Ilias
    2021 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2021, : 819 - 824
  • [42] Use of two-layer neural networks to answer scientific questions in radiation oncology
    Schmelz, Helmut
    Eich, Hans Theodor
    Haverkamp, Uwe
    Rehn, Stephan
    Hering, Dominik
    STRAHLENTHERAPIE UND ONKOLOGIE, 2023, 199 : S66 - S66
  • [43] Cumulant-based training algorithms of two-layer feedforward neural networks
    Dai, XH
    SIGNAL PROCESSING, 2000, 80 (08) : 1597 - 1606
  • [44] On the learning dynamics of two-layer quadratic neural networks for understanding deep learning
    Tan, Zhenghao
    Chen, Songcan
    FRONTIERS OF COMPUTER SCIENCE, 2022, 16 (03)
  • [45] Improved learning algorithm for two-layer neural networks for identification of nonlinear systems
    Vargas, Jose A. R.
    Pedrycz, Witold
    Hemerly, Elder M.
    NEUROCOMPUTING, 2019, 329 : 86 - 96
  • [46] Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
    Liu, Fanghui
    Dadi, Leello
    Cevher, Volkan
    JOURNAL OF MACHINE LEARNING RESEARCH, 2024, 25 : 1 - 42
  • [47] A Riemannian mean field formulation for two-layer neural networks with batch normalization
    Chao Ma
    Lexing Ying
    Research in the Mathematical Sciences, 2022, 9
  • [48] A Riemannian mean field formulation for two-layer neural networks with batch normalization
    Ma, Chao
    Ying, Lexing
    RESEARCH IN THE MATHEMATICAL SCIENCES, 2022, 9 (03)
  • [49] A heuristic two-layer reinforcement learning algorithm based on BP neural networks
    Liu, Zhibin
    Zeng, Xiaoqin
    Liu, Huiyi
    Chu, Rong
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2015, 52 (03): : 579 - 587
  • [50] Fast Signal Subspace Tracking Using Two-Layer Linear Neural Network Learning
    Valizadeh, Amir
    Farrokhrooz, Mehdi
    Rafiei, Ali
    2008 MEDITERRANEAN CONFERENCE ON CONTROL AUTOMATION, VOLS 1-4, 2008, : 1615 - 1619
12345