Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Unified Perspective on Multi-Domain and Multi-Task Learning   In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different ways of constructing semantic descriptors. Our interpretation
provides an alternative pipeline for zero-shot learning (ZSL), where a model
for a novel class can be constructed without training data. Moreover, it leads
to a new and practically relevant problem setting of zero-shot domain
adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model
for an unseen domain can be generated by its semantic descriptor. Experiments
across this range of problems demonstrate that our framework outperforms a
variety of alternatives.

12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con