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)
Incoming Non-self Citations Over Time
Authors
- 1. Kang Zhao (Alibaba)
- 2. Liuyihan Song (Alibaba)
- 3. Yingya Zhang (Alibaba)
- 4. Pan Pan (Alibaba)
- 5. Yinghui Xu (Alibaba)
- 6. Rong Jin (Alibaba)
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)
Showing 1 of 1 citing papers.
| 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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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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