Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying and interpreting tuning dimensions in deep networks   In neuroscience, a tuning dimension is a stimulus attribute that accounts for
much of the activation variance of a group of neurons. These are commonly used
to decipher the responses of such groups. While researchers have attempted to
manually identify an analogue to these tuning dimensions in deep neural
networks, we are unaware of an automatic way to discover them. This work
contributes an unsupervised framework for identifying and interpreting "tuning
dimensions" in deep networks. Our method correctly identifies the tuning
dimensions of a synthetic Gabor filter bank and tuning dimensions of the first
two layers of InceptionV1 trained on ImageNet.

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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th