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ANN Softmax: Acceleration of Extreme Classification Training

Summary: ANN Softmax combines binary-quantized inverted-file retrieval, GPU kernels, and sample grouping to select high-recall classes while avoiding full extreme-classification softmax. It matches full-softmax accuracy with 1/10 classes, delivers 4.3× faster training, and scales to 300M classes. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12801
Venue
VLDB
Year
2022
Pagerank
5.3573227e-05
Overall Rank
8,855 | 39.25%
DOI
10.14778/3485450.3485451

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhao_vldb22,
        title = {{ANN Softmax: Acceleration of Extreme Classification Training}},
        author = {Zhao, Kang and Song, Liuyihan and Zhang, Yingya and Pan, Pan and Xu, Yinghui and Jin, Rong},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {1},
        pages = {1--10},
        doi = {10.14778/3485450.3485451},
        url = {https://doi.org/10.14778/3485450.3485451},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
6,691 Dumpy: A Compact and Adaptive Index for Large Data Series Collections 2023 SIGMOD 5.8011086e-05
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