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In many learning situations, resources at inference time are significantly more constrained than resources at training time.
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Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L. Bartlett and Shahar Mendelson · 2003
Earlier work this paper cites.
Convolutional deep belief networks on cifar-10
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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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Earlier work this paper cites.
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Earlier work this paper cites.
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https://gluon-cv.mxnet.io/model_zoo/classification.html , 2018
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