2015 DeepLearninginNeuralNetworksAnO

• (Schmidhuber, 2015) ⇒ Jürgen Schmidhuber. (2015). “Deep Learning in Neural Networks: An Overview.” In: Neural Networks, 61.

Subject Headings: Deep Learning Algorithm.

Notes

• arXiv:1404.7828
• DOI: 10.1016/j.neunet.2014.09.003

Cited By

• Google Scholar: ~ 11,893 Citations, Retrieved:2021-03-21.

Quotes

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.

BibTeX

@article{2015_DeepLearninginNeuralNetworksAnO,
  author    = {Jurgen Schmidhuber},
  title     = {Deep Learning in Neural Networks: An Overview},
  journal   = {Neural Networks},
  volume    = {61},
  pages     = {85--117},
  year      = {2015},
  url       = {https://doi.org/10.1016/j.neunet.2014.09.003},
  doi       = {10.1016/j.neunet.2014.09.003},
}

@article{2015_DeepLearninginNeuralNetworksAnO,

 author    = {Jurgen Schmidhuber},
 title     = {Deep Learning in Neural Networks: An Overview},
 journal   = {CoRR},
 volume    = {abs/1404.7828},
 year      = {2014},
 url       = {http://arxiv.org/abs/1404.7828},
 archivePrefix = {arXiv},
 eprint    = {1404.7828},

}

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DeepLearninginNeuralNetworksAnO	Jürgen Schmidhuber			Deep Learning in Neural Networks: An Overview						2015