Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Provably Robust Models by Polyhedral Envelope Regularization   Training certifiable neural networks enables one to obtain models with
robustness guarantees against adversarial attacks. In this work, we introduce a
framework to bound the adversary-free region in the neighborhood of the input
data by a polyhedral envelope, which yields finer-grained certified robustness.
We further introduce polyhedral envelope regularization (PER) to encourage
larger polyhedral envelopes and thus improve the provable robustness of the
models. We demonstrate the flexibility and effectiveness of our framework on
standard benchmarks; it applies to networks of different architectures and
general activation functions. Compared with the state-of-the-art methods, PER
has very little computational overhead and better robustness guarantees without
over-regularizing the model.

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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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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-fin
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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: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat