Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Example Difficulty Using Variance of Gradients   In machine learning, a question of great interest is understanding what
examples are challenging for a model to classify. Identifying atypical examples
ensures the safe deployment of models, isolates samples that require further
human inspection and provides interpretability into model behavior. In this
work, we propose Variance of Gradients (VoG) as a valuable and efficient metric
to rank data by difficulty and to surface a tractable subset of the most
challenging examples for human-in-the-loop auditing. We show that data points
with high VoG scores are far more difficult for the model to learn and
over-index on corrupted or memorized examples. Further, restricting the
evaluation to the test set instances with the lowest VoG improves the model's
generalization performance. Finally, we show that VoG is a valuable and
efficient ranking for out-of-distribution detection.

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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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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: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg