Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence and Implicit Regularization Properties of Gradient Descent
  for Deep Residual Networks   We prove linear convergence of gradient descent to a global minimum for the
training of deep residual networks with constant layer width and smooth
activation function. We further show that the trained weights, as a function of
the layer index, admits a scaling limit which is H\"older continuous as the
depth of the network tends to infinity. The proofs are based on non-asymptotic
estimates of the loss function and of norms of the network weights along the
gradient descent path. We illustrate the relevance of our theoretical results
to practical settings using detailed numerical experiments on supervised
learning problems.

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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat