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Convolutional neural networks (CNN) have led to many state-of-the-art results spanning through various fields.
Lower bounds on the maximum cross correlation of signals (corresp.)
Lloyd R Welch · 1974
Earlier work this paper cites.
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Sheng Chen, Stephen A Billings, and Wan Luo · 1989
Earlier work this paper cites.
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B Boser LeCun, John S Denker, D Henderson, Richard E Howard, W Hubbard, and Lawrence D Jackel · 1990
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