Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augmenting and Tuning Knowledge Graph Embeddings   Knowledge graph embeddings rank among the most successful methods for link
prediction in knowledge graphs, i.e., the task of completing an incomplete
collection of relational facts. A downside of these models is their strong
sensitivity to model hyperparameters, in particular regularizers, which have to
be extensively tuned to reach good performance [Kadlec et al., 2017]. We
propose an efficient method for large scale hyperparameter tuning by
interpreting these models in a probabilistic framework. After a model
augmentation that introduces per-entity hyperparameters, we use a variational
expectation-maximization approach to tune thousands of such hyperparameters
with minimal additional cost. Our approach is agnostic to details of the model
and results in a new state of the art in link prediction on standard benchmark
data.

31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess