Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Equivariant Entity-Relationship Networks   The relational model is a ubiquitous representation of big-data, in part due
to its extensive use in databases. In this paper, we propose the Equivariant
Entity-Relationship Network (EERN), which is a Multilayer Perceptron
equivariant to the symmetry transformations of the Entity-Relationship model.
To this end, we identify the most expressive family of linear maps that are
exactly equivariant to entity relationship symmetries, and further show that
they subsume recently introduced equivariant maps for sets, exchangeable
tensors, and graphs. The proposed feed-forward layer has linear complexity in
the data and can be used for both inductive and transductive reasoning about
relational databases, including database embedding, and the prediction of
missing records. This provides a principled theoretical foundation for the
application of deep learning to one of the most abundant forms of data.
Empirically, EERN outperforms different variants of coupled matrix tensor
factorization in both synthetic and real-data experiments.

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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an