Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quadratic number of nodes is sufficient to learn a dataset via gradient
  descent   We prove that if an activation function satisfies some mild conditions and
number of neurons in a two-layered fully connected neural network with this
activation function is beyond a certain threshold, then gradient descent on
quadratic loss function finds the optimal weights of input layer for global
minima in linear time. This threshold value is an improvement over previously
obtained values. We hypothesise that this bound cannot be improved by the
method we are using in this work.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an