Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Trace Norm Regularised Deep Multi-Task Learning   We propose a framework for training multiple neural networks simultaneously.
The parameters from all models are regularised by the tensor trace norm, so
that each neural network is encouraged to reuse others' parameters if possible
-- this is the main motivation behind multi-task learning. In contrast to many
deep multi-task learning models, we do not predefine a parameter sharing
strategy by specifying which layers have tied parameters. Instead, our
framework considers sharing for all shareable layers, and the sharing strategy
is learned in a data-driven way.

31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc