Silicon neural networks learn as they compute

被引：0
|
作者：
Paillet, G
机构：
来源：
LASER FOCUS WORLD | 1996年 / 32卷 / 08期
关键词：
D O I：
暂无
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
引用
收藏
页码：S17 / S19
页数：3
相关论文
共 50 条
  • [21] Asynchronous Evaluation as an Efficient and Natural Way to Compute Neural Networks
    Taouali, Wahiba
    Alexandre, Frederic
    Hutt, Axel
    Rougier, Nicolas P.
    NUMERICAL ANALYSIS AND APPLIED MATHEMATICS, VOLS 1 AND 2, 2009, 1168 : 554 - 558
  • [22] Using deep neural networks to compute the mass of forming planets
    Alibert, Y.
    Venturini, J.
    Astronomy and Astrophysics, 2019, 626
  • [23] Combining neural networks and metamaterials concept to compute temporal derivatives
    Knightley, Tony
    Macdonald, Ross Glyn
    Johnson-Richards, Christian
    Rogers, Will
    Yakovlev, Alex
    Pacheco-Pena, Victor
    2024 4TH URSI ATLANTIC RADIO SCIENCE MEETING, AT-RASC 2024, 2024,
  • [24] SILICON IMPLEMENTATIONS OF NEURAL NETWORKS
    MURRAY, AF
    IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING, 1991, 138 (01) : 3 - 12
  • [25] SILICON IMPLEMENTATIONS OF NEURAL NETWORKS
    MURRAY, AF
    FIRST IEE INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS, 1989, : 27 - 32
  • [26] Neural networks and silicon chips
    DelCorso, D
    Reyneri, L
    AEI AUTOMAZIONE ENERGIA INFORMAZIONE, 1997, 84 (05): : 53 - 61
  • [27] Do Convolutional Neural Networks Learn Class Hierarchy?
    Alsallakh, Bilal
    Jourabloo, Amin
    Ye, Mao
    Liu, Xiaoming
    Ren, Liu
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2018, 24 (01) : 152 - 162
  • [28] Parsimonious neural networks learn interpretable physical laws
    Saaketh Desai
    Alejandro Strachan
    Scientific Reports, 11
  • [29] Neural networks can learn to approximate autonomous flows
    Rodriguez, Jose M.
    Garzon, Max H.
    FLUIDS AND WAVES: RECENT TRENDS IN APPLIED ANALYSIS, 2007, 440 : 197 - 206
  • [30] Parsimonious neural networks learn interpretable physical laws
    Desai, Saaketh
    Strachan, Alejandro
    SCIENTIFIC REPORTS, 2021, 11 (01)
12345