Deep learning in neural networks: An overview
J Schmidhuber - Neural networks, 2015 - Elsevier
Neural networks, 2015•Elsevier
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 …
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 …
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.
Elsevier