Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NFT-K: Non-Fungible Tangent Kernels   Deep neural networks have become essential for numerous applications due to
their strong empirical performance such as vision, RL, and classification.
Unfortunately, these networks are quite difficult to interpret, and this limits
their applicability in settings where interpretability is important for safety,
such as medical imaging. One type of deep neural network is neural tangent
kernel that is similar to a kernel machine that provides some aspect of
interpretability. To further contribute interpretability with respect to
classification and the layers, we develop a new network as a combination of
multiple neural tangent kernels, one to model each layer of the deep neural
network individually as opposed to past work which attempts to represent the
entire network via a single neural tangent kernel. We demonstrate the
interpretability of this model on two datasets, showing that the multiple
kernels model elucidates the interplay between the layers and predictions.

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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
6
The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
2
The categories of this paper are: math
3
The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg