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web.archive.org
web.archive.org › web › 20...o.org › record › 1258627
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zenodo.org
zenodo.org › record › 1258627
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arxiv.org
arxiv.org › abs › 1404.7828
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...
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www.ncbi.nlm.nih.gov
ncbi.nlm.nih.gov › pubmed › 9377276
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's ...
www.nature.com
nature.com › articles › nature14539
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved...
ui.adsabs.harvard.edu
ui.adsabs.harvard.edu › abs › 2015Natur.521..436L
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved...
doi.org
doi.org › 10.1038%2Fnature14539
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved...
stackoverflow.com
stackoverflow.com › ques...-symbolic-expression
Tags: tensorflow, pytorch, theano, backpropagation, automatic-differentiation | Score: 5 | Answered: Yes
en.wikipedia.org
en.wikipedia.org › wiki › Long_short-term_memory
and Schmidhuber published LSTM with full backpropagation through time and bidirectional LSTM. In 2006 Graves, Fernandez, Gomez, and Schmidhuber introduced
www.cs.unm.edu
cs.unm.edu › ~luger › ai-final › tocfull.html
George Luger's Artificial Intelligence