Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradients of Counterfactuals   Gradients have been used to quantify feature importance in machine learning
models. Unfortunately, in nonlinear deep networks, not only individual neurons
but also the whole network can saturate, and as a result an important input
feature can have a tiny gradient. We study various networks, and observe that
this phenomena is indeed widespread, across many inputs.
  We propose to examine interior gradients, which are gradients of
counterfactual inputs constructed by scaling down the original input. We apply
our method to the GoogleNet architecture for object recognition in images, as
well as a ligand-based virtual screening network with categorical features and
an LSTM based language model for the Penn Treebank dataset. We visualize how
interior gradients better capture feature importance. Furthermore, interior
gradients are applicable to a wide variety of deep networks, and have the
attribution property that the feature importance scores sum to the the
prediction score.
  Best of all, interior gradients can be computed just as easily as gradients.
In contrast, previous methods are complex to implement, which hinders practical
adoption.

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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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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: comp-gas