Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Task Learning as Multi-Objective Optimization   In multi-task learning, multiple tasks are solved jointly, sharing inductive
bias between them. Multi-task learning is inherently a multi-objective problem
because different tasks may conflict, necessitating a trade-off. A common
compromise is to optimize a proxy objective that minimizes a weighted linear
combination of per-task losses. However, this workaround is only valid when the
tasks do not compete, which is rarely the case. In this paper, we explicitly
cast multi-task learning as multi-objective optimization, with the overall
objective of finding a Pareto optimal solution. To this end, we use algorithms
developed in the gradient-based multi-objective optimization literature. These
algorithms are not directly applicable to large-scale learning problems since
they scale poorly with the dimensionality of the gradients and the number of
tasks. We therefore propose an upper bound for the multi-objective loss and
show that it can be optimized efficiently. We further prove that optimizing
this upper bound yields a Pareto optimal solution under realistic assumptions.
We apply our method to a variety of multi-task deep learning problems including
digit classification, scene understanding (joint semantic segmentation,
instance segmentation, and depth estimation), and multi-label classification.
Our method produces higher-performing models than recent multi-task learning
formulations or per-task training.

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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph