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Deep learning in neural networks:An overview - 2015

Research Area:  Machine Learning

Abstract:

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.

Author(s) Name:  J├╝rgen Schmidhuber

Journal name:  Neural Networks

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.neunet.2014.09.003

Volume Information:   Volume 61, January 2015, Pages 85-117