From Perceptrons to Deep Neural Networks

被引：0
|
作者：
Lacko, Peter [1 ]
机构：
[1] Slovak Univ Technol Bratislava, Fac Informat & Informat Technol, Bratislava, Slovakia
来源：
2017 IEEE 15TH INTERNATIONAL SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI) | 2017年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Deep neural networks are intensively researched field of artificial intelligence. Big companies like Google, Microsoft, Baidu or Facebook are supporting research and development in this field. The recent victory over human player in the game of Go points to a huge potential of this approach. Machine learning approaches based on deep learning techniques bring significant gain over existing methods based on manually tuned features in different areas. In this paper we present the evolution of deep neural networks from first neuron models towards today's deep architectures.
引用
收藏
页码：169 / 172
页数：4
相关论文
共 50 条
  • [1] TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding
    Zhang, Lei
    Zhou, Shengyuan
    Zhi, Tian
    Du, Zidong
    Chen, Yunji
    THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, : 1319 - 1326
  • [2] Approximation Capabilities of Neural Networks using Morphological Perceptrons and Generalizations
    Chang, William
    Hamad, Hassan
    Chugg, Keith M.
    2022 56TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, 2022, : 770 - 776
  • [3] ARTIFICIAL NEURAL NETWORKS - SIMULATION OF N-LAYER PERCEPTRONS
    CARDARILLI, G
    LOJACONO, R
    PASERO, E
    SALERNO, M
    SALSANO, A
    SUBIACO, G
    SIMULATION METHODOLOGIES, LANGUAGES AND ARCHITECTURES AND AI AND GRAPHICS FOR SIMULATION, 1989, : 41 - 46
  • [4] Using multilayer perceptrons as receptive fields in the design of neural networks
    Cimino, Mario G. C. A.
    Pedrycz, Witold
    Lazzerini, Beatrice
    Marcelloni, Francesco
    NEUROCOMPUTING, 2009, 72 (10-12) : 2536 - 2548
  • [5] Beyond multilayer perceptrons: Investigating complex topologies in neural networks
    Boccato, Tommaso
    Ferrante, Matteo
    Duggento, Andrea
    Toschi, Nicola
    NEURAL NETWORKS, 2024, 171 : 215 - 228
  • [6] On optimizing large neural networks (multilayer perceptrons) by learning and evolution
    Braun, H
    ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 1996, 76 : 211 - 214
  • [7] Generalized Model of Biological Neural Networks: Progressive Operational Perceptrons
    Kiranyaz, Serkan
    Ince, Turker
    Iosifidis, Alexandros
    Gabbouj, Moncef
    2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 2477 - 2485
  • [8] DSNNs:learning transfer from deep neural networks to spiking neural networks
    张磊
    Du Zidong
    Li Ling
    Chen Yunji
    HighTechnologyLetters, 2020, 26 (02) : 136 - 144
  • [9] DSNNs: learning transfer from deep neural networks to spiking neural networks
    Zhang L.
    Du Z.
    Li L.
    Chen Y.
    High Technology Letters, 2020, 26 (02): : 136 - 144
  • [10] USING QUADRATIC PERCEPTRONS TO REDUCE INTERCONNECTION DENSITY IN MULTILAYER NEURAL NETWORKS
    ROCKMANN, D
    MORAGA, C
    LECTURE NOTES IN COMPUTER SCIENCE, 1991, 540 : 86 - 92
12345