Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TREC Deep Learning Track: Reusable Test Collections in the Large Data
  Regime   The TREC Deep Learning (DL) Track studies ad hoc search in the large data
regime, meaning that a large set of human-labeled training data is available.
Results so far indicate that the best models with large data may be deep neural
networks. This paper supports the reuse of the TREC DL test collections in
three ways. First we describe the data sets in detail, documenting clearly and
in one place some details that are otherwise scattered in track guidelines,
overview papers and in our associated MS MARCO leaderboard pages. We intend
this description to make it easy for newcomers to use the TREC DL data. Second,
because there is some risk of iteration and selection bias when reusing a data
set, we describe the best practices for writing a paper using TREC DL data,
without overfitting. We provide some illustrative analysis. Finally we address
a number of issues around the TREC DL data, including an analysis of
reusability.

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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: math
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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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: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom