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Deep learning methods continue to have a decided impact on machine learning, both in theory and in practice.
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Butucea, C. (2007) · 1930
Earlier work this paper cites.
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Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
Earlier work this paper cites.
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Vitushkin, A. G. (1964) · 1964
Earlier work this paper cites.
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Dawid, A. P. (1982) · 1982
Earlier work this paper cites.
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Ibragimov, I. A. and Khasminskii, R. Z. (1985) · 1985
Earlier work this paper cites.
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Neal, R. M. (1993) · 1993
Earlier work this paper cites.
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Efromovich, S. and Low, M. G. (1996) · 1996
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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Earlier work this paper cites.
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Anthony, M. and Bartlett, P. L. (2009) · 2009
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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Later among the works it cites.
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