Implementation of evolutionary algorithms for deep architectures
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Tirumala, Sreenivas Sremath
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Date
2014-11
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Conference Contribution - Paper in Published Proceedings
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Keyword
deep architectures
deep learning
evolutionary algorithms
deep learning
evolutionary algorithms
ANZSRC Field of Research Code (2020)
Citation
Tirumala, S. S. (2014). Implementation of Evolutionary Algorithms for Deep Architectures. Antonio Lieto, Daniele P. Radicioni, Marco Cruciani:(Ed.), Proceedings of the Second International Workshop on Artificial Intelligence and Cognition (AIC 2014) (pp.164-171).
Abstract
Deep learning is becoming an increasingly interesting and powerful machine learning method with successful applications in many domains, such as natural language processing, image recognition, and hand-written character recognition. Despite of its eminent success, limitations of traditional learning approach may still prevent deep learning from achieving a wide range of realistic learning tasks. Due to their flexibility and proven effectiveness, evolutionary learning techniques may therefore play a crucial role towards unleashing the full potential of deep learning in practice. Unfortunately, many researchers with a strong background on evolutionary computation are not fully aware of the state-ofthe-art research on deep learning. To meet this knowledge gap and to promote the research on evolutionary inspired deep learning techniques, this paper presents a comprehensive review of the latest deep architectures and surveys important evolutionary algorithms that can potentially be explored for training these deep architectures.
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CEUR Workshop Proceedings
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