Deep learning in neural networks: An overview

@article{Schmidhuber2015DeepLI,
  title={Deep learning in neural networks: An overview},
  author={J. Schmidhuber},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2015},
  volume={61},
  pages={
          85-117
        }
}
  • J. Schmidhuber
  • Published 2015
  • Computer Science, Medicine
  • Neural networks : the official journal of the International Neural Network Society
  • 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… CONTINUE READING
    Deep Reinforcement Learning: An Overview
    21
    Neural network models and deep learning
    12
    Recent Advances in Deep Learning: An Overview
    25
    Computational Functionalism for the Deep Learning Era
    6

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 1,137 REFERENCES
    Adaptive dropout for training deep neural networks
    197
    Unsupervised Learning Procedures for Neural Networks
    86
    Neural Networks: Tricks of the Trade
    630
    Learning Long-Term Dependencies with
    • Patrice Simardy, Paolo FrasconizyAT
    • 2007
    48