Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Invariants through Soft Unification   Human reasoning involves recognising common underlying principles across many
examples. The by-products of such reasoning are invariants that capture
patterns such as "if someone went somewhere then they are there", expressed
using variables "someone" and "somewhere" instead of mentioning specific people
or places. Humans learn what variables are and how to use them at a young age.
This paper explores whether machines can also learn and use variables solely
from examples without requiring human pre-engineering. We propose Unification
Networks, an end-to-end differentiable neural network approach capable of
lifting examples into invariants and using those invariants to solve a given
task. The core characteristic of our architecture is soft unification between
examples that enables the network to generalise parts of the input into
variables, thereby learning invariants. We evaluate our approach on five
datasets to demonstrate that learning invariants captures patterns in the data
and can improve performance over baselines.

12
The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an