Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Common-Description Learning: A Framework for Learning Algorithms and
  Generating Subproblems from Few Examples   Current learning algorithms face many difficulties in learning simple
patterns and using them to learn more complex ones. They also require more
examples than humans do to learn the same pattern, assuming no prior knowledge.
In this paper, a new learning framework is introduced that is called
common-description learning (CDL). This framework has been tested on 32 small
multi-task datasets, and the results show that it was able to learn complex
algorithms from a few number of examples. The final model is perfectly
interpretable and its depth depends on the question. What is meant by depth
here is that whenever needed, the model learns to break down the problem into
simpler subproblems and solves them using previously learned models. Finally,
we explain the capabilities of our framework in discovering complex relations
in data and how it can help in improving language understanding in machines.

35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat