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Challenges in deep learning

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Publication date27/04/2016
Host publicationESANN 2016 - 24th European Symposium on Artificial Neural Networks
Publisheri6doc.com publication
Pages489-496
Number of pages8
ISBN (electronic)9782875870278
<mark>Original language</mark>English
Event24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 - Bruges, Belgium
Duration: 27/04/201629/04/2016

Conference

Conference24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016
Country/TerritoryBelgium
CityBruges
Period27/04/1629/04/16

Publication series

NameESANN 2016 - 24th European Symposium on Artificial Neural Networks

Conference

Conference24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016
Country/TerritoryBelgium
CityBruges
Period27/04/1629/04/16

Abstract

In recent years, Deep Learning methods and architectures have reached impressive results, allowing quantum-leap improvements in performance in many difficult tasks, such as speech recognition, end-to-end machine translation, image classification/understanding, just to name a few. After a brief introduction to some of the main achievements of Deep Learning, we discuss what we think are the general challenges that should be addressed in the future. We close with a review of the contributions to the ESANN 2016 special session on Deep Learning.