Implementation of evolutionary algorithms for deep architectures
Tirumala, Sreenivas Sremath
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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:http://hdl.handle.net/10652/3963
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.