Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicit Regularization via Neural Feature Alignment   We approach the problem of implicit regularization in deep learning from a
geometrical viewpoint. We highlight a regularization effect induced by a
dynamical alignment of the neural tangent features introduced by Jacot et al,
along a small number of task-relevant directions. This can be interpreted as a
combined mechanism of feature selection and compression. By extrapolating a new
analysis of Rademacher complexity bounds for linear models, we motivate and
study a heuristic complexity measure that captures this phenomenon, in terms of
sequences of tangent kernel classes along optimization paths.

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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas