TR2015-032
Structure Discovery of Deep Neural Network Based on Evolutionary Algorithms
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- "Structure Discovery of Deep Neural Network Based on Evolutionary Algorithms", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2015.7178918, April 2015, pp. 4979-4983.BibTeX TR2015-032 PDF
- @inproceedings{Shinozaki2015apr,
- author = {Shinozaki, T. and Watanabe, S.},
- title = {Structure Discovery of Deep Neural Network Based on Evolutionary Algorithms},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2015,
- pages = {4979--4983},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP.2015.7178918},
- url = {https://www.merl.com/publications/TR2015-032}
- }
,
- "Structure Discovery of Deep Neural Network Based on Evolutionary Algorithms", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2015.7178918, April 2015, pp. 4979-4983.
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Research Areas:
Abstract:
Deep neural networks (DNNs) are constructed by considering highly complicated configurations including network structure and several tuning parameters (number of hidden states and learning rate in each layer), which greatly affect the performance of speech processing applications. To reach optimal performance in such systems, deep understanding and expertise in DNNs is necessary, which limits the development of DNN systems to skilled experts. To overcome the problem, this paper proposes an efficient optimization strategy for DNN structure and parameters using evolutionary algorithms. The proposed approach parametrizes the DNN structure by a directed acyclic graph, and the DNN structure is represented by a simple binary vector. Genetic algorithm and covariance matrix adaptation evolution strategy efficiently optimize the performance jointly with respect to the above binary vector and the other tuning parameters. Experiments on phoneme recognition and spoken digit detection tasks show the effectiveness of the proposed approach by discovering the appropriate DNN structure automatically.
Related News & Events
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NEWS Multimedia Group researchers presented 8 papers at ICASSP 2015 Date: April 19, 2015 - April 24, 2015
Where: IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP)
MERL Contacts: Anthony Vetro; Hassan Mansour; Petros T. Boufounos; Jonathan Le RouxBrief- Multimedia Group researchers have presented 8 papers at the recent IEEE International Conference on Acoustics, Speech & Signal Processing, which was held in Brisbane, Australia from April 19-24, 2015.