Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Hierarchical Information Flow with Recurrent Neural Modules   We propose ThalNet, a deep learning model inspired by neocortical
communication via the thalamus. Our model consists of recurrent neural modules
that send features through a routing center, endowing the modules with the
flexibility to share features over multiple time steps. We show that our model
learns to route information hierarchically, processing input data by a chain of
modules. We observe common architectures, such as feed forward neural networks
and skip connections, emerging as special cases of our architecture, while
novel connectivity patterns are learned for the text8 compression task. Our
model outperforms standard recurrent neural networks on several sequential
benchmarks.

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The categories of this paper are: gr-qc
3
The categories of this paper are: physics
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
2
The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th