Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AutoLoss: Learning Discrete Schedules for Alternate Optimization   Many machine learning problems involve iteratively and alternately optimizing
different task objectives with respect to different sets of parameters.
Appropriately scheduling the optimization of a task objective or a set of
parameters is usually crucial to the quality of convergence. In this paper, we
present AutoLoss, a meta-learning framework that automatically learns and
determines the optimization schedule. AutoLoss provides a generic way to
represent and learn the discrete optimization schedule from metadata, allows
for a dynamic and data-driven schedule in ML problems that involve alternating
updates of different parameters or from different loss objectives. We apply
AutoLoss on four ML tasks: d-ary quadratic regression, classification using a
multi-layer perceptron (MLP), image generation using GANs, and multi-task
neural machine translation (NMT). We show that the AutoLoss controller is able
to capture the distribution of better optimization schedules that result in
higher quality of convergence on all four tasks. The trained AutoLoss
controller is generalizable -- it can guide and improve the learning of a new
task model with different specifications, or on different datasets.

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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: econ