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In many real-world applications, we often need to handle various deployment scenarios, where the resource constraint and the superclass of interest corresponding to a group of classes are dynamically specified.
Discovering structure in multiple learning tasks: The tc algorithm
Sebastian Thrun and Joseph O’Sullivan · 1996
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
Ning Qian · 1999
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Rie Kubota Ando, Tong Zhang, and Peter Bartlett · 2005
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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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Adam: A method for stochastic optimization
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Earlier work this paper cites.
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Earlier work this paper cites.
Runtime neural pruning
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