Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Relational Representations with Auto-encoding Logic Programs   Deep learning methods capable of handling relational data have proliferated
over the last years. In contrast to traditional relational learning methods
that leverage first-order logic for representing such data, these deep learning
methods aim at re-representing symbolic relational data in Euclidean spaces.
They offer better scalability, but can only numerically approximate relational
structures and are less flexible in terms of reasoning tasks supported. This
paper introduces a novel framework for relational representation learning that
combines the best of both worlds. This framework, inspired by the auto-encoding
principle, uses first-order logic as a data representation language, and the
mapping between the original and latent representation is done by means of
logic programs instead of neural networks. We show how learning can be cast as
a constraint optimisation problem for which existing solvers can be used. The
use of logic as a representation language makes the proposed framework more
accurate (as the representation is exact, rather than approximate), more
flexible, and more interpretable than deep learning methods. We experimentally
show that these latent representations are indeed beneficial in relational
learning tasks.

8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con