Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Multiple Tasks with Multilinear Relationship Networks   Deep networks trained on large-scale data can learn transferable features to
promote learning multiple tasks. Since deep features eventually transition from
general to specific along deep networks, a fundamental problem of multi-task
learning is how to exploit the task relatedness underlying parameter tensors
and improve feature transferability in the multiple task-specific layers. This
paper presents Multilinear Relationship Networks (MRN) that discover the task
relationships based on novel tensor normal priors over parameter tensors of
multiple task-specific layers in deep convolutional networks. By jointly
learning transferable features and multilinear relationships of tasks and
features, MRN is able to alleviate the dilemma of negative-transfer in the
feature layers and under-transfer in the classifier layer. Experiments show
that MRN yields state-of-the-art results on three multi-task learning datasets.

23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph