Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Robust Unsupervised Ensemble of Feature-Based Explanations using
  Restricted Boltzmann Machines   Understanding the results of deep neural networks is an essential step
towards wider acceptance of deep learning algorithms. Many approaches address
the issue of interpreting artificial neural networks, but often provide
divergent explanations. Moreover, different hyperparameters of an explanatory
method can lead to conflicting interpretations. In this paper, we propose a
technique for aggregating the feature attributions of different explanatory
algorithms using Restricted Boltzmann Machines (RBMs) to achieve a more
reliable and robust interpretation of deep neural networks. Several challenging
experiments on real-world datasets show that the proposed RBM method
outperforms popular feature attribution methods and basic ensemble techniques.

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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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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: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph