Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAM: The Sensitivity of Attribution Methods to Hyperparameters   Attribution methods can provide powerful insights into the reasons for a
classifier's decision. We argue that a key desideratum of an explanation method
is its robustness to input hyperparameters which are often randomly set or
empirically tuned. High sensitivity to arbitrary hyperparameter choices does
not only impede reproducibility but also questions the correctness of an
explanation and impairs the trust of end-users. In this paper, we provide a
thorough empirical study on the sensitivity of existing attribution methods. We
found an alarming trend that many methods are highly sensitive to changes in
their common hyperparameters e.g. even changing a random seed can yield a
different explanation! Interestingly, such sensitivity is not reflected in the
average explanation accuracy scores over the dataset as commonly reported in
the literature. In addition, explanations generated for robust classifiers
(i.e. which are trained to be invariant to pixel-wise perturbations) are
surprisingly more robust than those generated for regular classifiers.

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The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
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The categories of this paper are: stat
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
19
The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph