Deep learning in neural networks: An overview

被引：11084

作者：

Schmidhuber, Juergen ^{[1
,2
]}

机构：

[1] Univ Lugano, Ist Dalle Molle Studi Intelligenza Artificialale, Swiss AI Lab IDSIA, CH-6928 Manno Lugano, Switzerland

[2] SUPSI, CH-6928 Manno Lugano, Switzerland

来源：

NEURAL NETWORKS | 2015年 / 61卷

关键词：

Deep learning; Supervised learning; Unsupervised learning; Reinforcement learning; Evolutionary computation; PRINCIPAL COMPONENT ANALYSIS; LONG-TERM DEPENDENCIES; INFERIOR TEMPORAL CORTEX; SLOW FEATURE ANALYSIS; RECEPTIVE-FIELDS; SPIKING NEURONS; PATTERN-RECOGNITION; OBJECT RECOGNITION; FEATURE-EXTRACTION; SELF-ORGANIZATION;

D O I：

10.1016/j.neunet.2014.09.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. (C) 2014 Published by Elsevier Ltd.

引用

页码：85 / 117

页数：33