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Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.
The uncertainty principle: a mathematical survey
Gerald B Folland and Alladi Sitaram · 1997
Earlier work this paper cites.
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Earlier work this paper cites.
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Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
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Earlier work this paper cites.
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Naum I Achieser · 2013
Earlier work this paper cites.
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Ameya Agaskar and Yue M Lu · 2013
Earlier work this paper cites.
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David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2013
Earlier work this paper cites.
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Lloyd N Trefethen · 2013
Earlier work this paper cites.
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Ron Levie, Federico Monti, Xavier Bresson, and Michael M Bronstein · 2018
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