Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: In Defense of the Unitary Scalarization for Deep Multi-Task Learning   Recent multi-task learning research argues against unitary scalarization,
where training simply minimizes the sum of the task losses. Several ad-hoc
multi-task optimization algorithms have instead been proposed, inspired by
various hypotheses about what makes multi-task settings difficult. The majority
of these optimizers require per-task gradients, and introduce significant
memory, runtime, and implementation overhead. We present a theoretical analysis
suggesting that many specialized multi-task optimizers can be interpreted as
forms of regularization. Moreover, we show that, when coupled with standard
regularization and stabilization techniques from single-task learning, unitary
scalarization matches or improves upon the performance of complex multi-task
optimizers in both supervised and reinforcement learning settings. We believe
our results call for a critical reevaluation of recent research in the area.

2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg