Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicitly Maximizing Margins with the Hinge Loss   A new loss function is proposed for neural networks on classification tasks
which extends the hinge loss by assigning gradients to its critical points. We
will show that for a linear classifier on linearly separable data with fixed
step size, the margin of this modified hinge loss converges to the $\ell_2$
max-margin at the rate of $\mathcal{O}( 1/t )$. This rate is fast when compared
with the $\mathcal{O}(1/\log t)$ rate of exponential losses such as the
logistic loss. Furthermore, empirical results suggest that this increased
convergence speed carries over to ReLU networks.

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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
2
The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph