Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Discoveries of Physical and Semantic Concepts via Association
  Priors of Neuron Groups   The recent successful deep neural networks are largely trained in a
supervised manner. It {\it associates} complex patterns of input samples with
neurons in the last layer, which form representations of {\it concepts}. In
spite of their successes, the properties of complex patterns associated a
learned concept remain elusive. In this work, by analyzing how neurons are
associated with concepts in supervised networks, we hypothesize that with
proper priors to regulate learning, neural networks can automatically associate
neurons in the intermediate layers with concepts that are aligned with real
world concepts, when trained only with labels that associate concepts with top
level neurons, which is a plausible way for unsupervised learning. We develop a
prior to verify the hypothesis and experimentally find the proposed prior help
neural networks automatically learn both basic physical concepts at the lower
layers, e.g., rotation of filters, and highly semantic concepts at the higher
layers, e.g., fine-grained categories of an entry-level category.

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The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: stat
14
The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci