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    Implementation of evolutionary algorithms for deep architectures

    Tirumala, Sreenivas Sremath

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    Implementation_of_EA_for_DA.pdf (978.2Kb)
    Date
    2014-11
    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).
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/3963
    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.
    Keywords:
    deep architectures, deep learning, evolutionary algorithms
    ANZSRC Field of Research:
    170203 Knowledge Representation and Machine Learning
    Copyright Holder:
    Author
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    Available Online at:
    http://ceur-ws.org/Vol-1315/paper15.pdf
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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