Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Data Selection for Curriculum Learning via Ability Estimation   Curriculum learning methods typically rely on heuristics to estimate the
difficulty of training examples or the ability of the model. In this work, we
propose replacing difficulty heuristics with learned difficulty parameters. We
also propose Dynamic Data selection for Curriculum Learning via Ability
Estimation (DDaCLAE), a strategy that probes model ability at each training
epoch to select the best training examples at that point. We show that models
using learned difficulty and/or ability outperform heuristic-based curriculum
learning models on the GLUE classification tasks.

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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an