Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ODE guided Neural Data Augmentation Techniques for Time Series Data and
  its Benefits on Robustness   Exploring adversarial attack vectors and studying their effects on machine
learning algorithms has been of interest to researchers. Deep neural networks
working with time series data have received lesser interest compared to their
image counterparts in this context. In a recent finding, it has been revealed
that current state-of-the-art deep learning time series classifiers are
vulnerable to adversarial attacks. In this paper, we introduce two local
gradient based and one spectral density based time series data augmentation
techniques. We show that a model trained with data obtained using our
techniques obtains state-of-the-art classification accuracy on various time
series benchmarks. In addition, it improves the robustness of the model against
some of the most common corruption techniques,such as Fast Gradient Sign Method
(FGSM) and Basic Iterative Method (BIM).

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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
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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: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga