ModsNet: Performance-aware Top-k Model Search using Exemplar Datasets
Summary: ModsNet performs query-by-example top-k search over pretrained data-science models, ranking expected task performance on an exemplar dataset via a model–dataset knowledge graph and bipartite GNN. A cost-bounded probe-and-select strategy addresses strict cold starts. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Mengying Wang (Case Western Reserve University)
- 2. Hanchao Ma (Case Western Reserve University)
- 3. Sheng Guan (Case Western Reserve University)
- 4. Yiyang Bian (Case Western Reserve University)
- 5. Haolai Che (Case Western Reserve University)
- 6. Abhishek Daundkar (Case Western Reserve University)
- 7. Alp Sehirlioglu (Case Western Reserve University)
- 8. Yinghui Wu (Case Western Reserve University)
BibTeX Citation
@article{wang_vldb24,
title = {{ModsNet: Performance-aware Top-k Model Search using Exemplar Datasets}},
author = {Wang, Mengying and Ma, Hanchao and Guan, Sheng and Bian, Yiyang and Che, Haolai and Daundkar, Abhishek and Sehirlioglu, Alp and Wu, Yinghui},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4457--4460},
doi = {10.14778/3685800.3685899},
url = {https://doi.org/10.14778/3685800.3685899},
year = {2024}
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 11,066 | ML-Asset Management: Curation, Discovery, and Utilization | 2025 | VLDB | 5.093636e-05 |
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