Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5773551 | Applied and Computational Harmonic Analysis | 2017 | 13 Pages |
Abstract
Multinomial logistic regression and other classification schemes used in conjunction with convolutional networks (convnets) were designed largely before the rise of the now standard coupling with convnets, stochastic gradient descent, and backpropagation. In the specific application to supervised learning for convnets, a simple scale-invariant classification stage is more robust than multinomial logistic regression, appears to result in somewhat lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning. “Scale-invariant” means that multiplying the input values by any nonzero real number leaves the output unchanged.
Keywords
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Physical Sciences and Engineering
Mathematics
Analysis
Authors
Soumith Chintala, Marc'Aurelio Ranzato, Arthur Szlam, Yuandong Tian, Mark Tygert, Wojciech Zaremba,