Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Powerful, transferable representations for molecules through intelligent
  task selection in deep multitask networks   Chemical representations derived from deep learning are emerging as a
powerful tool in areas such as drug discovery and materials innovation.
Currently, this methodology has three major limitations - the cost of
representation generation, risk of inherited bias, and the requirement for
large amounts of data. We propose the use of multi-task learning in tandem with
transfer learning to address these limitations directly. In order to avoid
introducing unknown bias into multi-task learning through the task selection
itself, we calculate task similarity through pairwise task affinity, and use
this measure to programmatically select tasks. We test this methodology on
several real-world data sets to demonstrate its potential for execution in
complex and low-data environments. Finally, we utilise the task similarity to
further probe the expressiveness of the learned representation through a
comparison to a commonly used cheminformatics fingerprint, and show that the
deep representation is able to capture more expressive task-based information.

15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th