Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robustness Analysis of Classification Using Recurrent Neural Networks
  with Perturbed Sequential Input   For a given stable recurrent neural network (RNN) that is trained to perform
a classification task using sequential inputs, we quantify explicit robustness
bounds as a function of trainable weight matrices. The sequential inputs can be
perturbed in various ways, e.g., streaming images can be deformed due to robot
motion or imperfect camera lens. Using the notion of the Voronoi diagram and
Lipschitz properties of stable RNNs, we provide a thorough analysis and
characterize the maximum allowable perturbations while guaranteeing the full
accuracy of the classification task. We illustrate and validate our theoretical
results using a map dataset with clouds as well as the MNIST dataset.

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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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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: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn