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We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.
Optimal brain damage
Yann Le Cun, John S. Denker, and Sara A. Solla · 1990
Earlier work this paper cites.
Switchboard: telephone speech corpus for research and development
John J. Godfrey, Edward C. Holliman, and Jane McDaniel · 1992
Earlier work this paper cites.
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S. J. Young, J. J. Odell, and P. C. Woodland · 1994
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Big neural networks waste capacity
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Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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