Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Relational Tucker Decomposition for Multi-Relational Link Prediction   We propose the Relational Tucker3 (RT) decomposition for multi-relational
link prediction in knowledge graphs. We show that many existing knowledge graph
embedding models are special cases of the RT decomposition with certain
predefined sparsity patterns in its components. In contrast to these prior
models, RT decouples the sizes of entity and relation embeddings, allows
parameter sharing across relations, and does not make use of a predefined
sparsity pattern. We use the RT decomposition as a tool to explore whether it
is possible and beneficial to automatically learn sparsity patterns, and
whether dense models can outperform sparse models (using the same number of
parameters). Our experiments indicate that---depending on the dataset--both
questions can be answered affirmatively.

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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn