Dear computational neuroscientists,
here the preliminary draft of an invited Deep Learning overview:
It mostly consists of references (about 800 entries so far). Important citations are still missing though. As a machine learning researcher, I am obsessed with credit assignment. In case you know of references to add or correct, please send them with brief explanations to firstname.lastname@example.org (NOT TO THE ENTIRE LIST!), preferably together with URL links to PDFs for verification. Please also do not hesitate to send me additional corrections / improvements / suggestions / Deep Learning success stories with feedforward and recurrent neural networks. I'll post a revised version later. Thanks a lot!
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 summarises 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.
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