Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fighting Redundancy and Model Decay with Embeddings   Every day, hundreds of millions of new Tweets containing over 40 languages of
ever-shifting vernacular flow through Twitter. Models that attempt to extract
insight from this firehose of information must face the torrential covariate
shift that is endemic to the Twitter platform. While regularly-retrained
algorithms can maintain performance in the face of this shift, fixed model
features that fail to represent new trends and tokens can quickly become stale,
resulting in performance degradation. To mitigate this problem we employ
learned features, or embedding models, that can efficiently represent the most
relevant aspects of a data distribution. Sharing these embedding models across
teams can also reduce redundancy and multiplicatively increase cross-team
modeling productivity. In this paper, we detail the commoditized tools,
algorithms and pipelines that we have developed and are developing at Twitter
to regularly generate high quality, up-to-date embeddings and share them
broadly across the company.

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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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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: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: nlin