Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber, “Deep Learning in Neural Networks: An Overview”. arXiv:1404.7828v1 (submitted 30 April 2014). arXiv:1404.7828v4 (the version read) (submitted 8 October 2014).
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- Deep Learning in Neural Networks: An Overview (arXiv:1404.7828v4) arXiv · published 2014-10-08 · Retrieved for the candidate pack on 2026-10-29 (SHA-256 9306f28e5eb896d2684114e7f0068d70fb6baaa2e26bbab47f686078013d309b). arXiv version 4 of 8 October 2014 (Technical Report IDSIA-03-14). The later journal version (Neural Networks, 2015) was not retrieved.
- Deep Learning in Neural Networks: An Overview (arXiv abstract page) arXiv · published 2014-10-08 · Retrieved for the candidate pack on 2026-10-29 (SHA-256 30983a34cd41597fa7adf057e7f17522b7dc11c3ad263548c4b21f8fb9afff00). arXiv abstract page with submission history, as served on 2026-10-29.
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- A fast learning algorithm for deep belief netsSchmidhuber's 2014 overview cites Hinton, Osindero and Teh (2006), with Hinton and Salakhutdinov (2006), in its sentence that the expression Deep Learning “was actually coined around 2006, when unsupervised pre-training of deep FNNs helped to accelerate subsequent SL through BP”.2014-10-08 · event · Stated in source · Supported Inspect claim & evidence ↗A fast learning algorithm for deep belief nets: document page →
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- The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors (master's thesis, University of Helsinki)According to Schmidhuber's 2014 overview, explicit, efficient error backpropagation in arbitrary, discrete, possibly sparse NN-like networks was apparently first described in Linnainmaa's 1970 master's thesis, without reference to neural networks.2014-10-08 · event · Stated in source · Supported Inspect claim & evidence ↗The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors (master's thesis, University of Helsinki): document page →
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