Alsatian: Optimizing Model Search for Deep Transfer Learning
Summary: Alsatian exploits shared model blocks, caching, and search-ordering to accelerate transfer-learning model search. This cache-aware, block-sharing approach reduces repeated inference across thousands of candidate models, yielding up to 14x speedups on CV/NLP benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Nils Strassenburg
- 2. Boris Glavic
- 3. Tilmann Rabl
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Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,565 | Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff | 2015 | VLDB | 0.00011336478 |
| 1,666 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010955907 |
| 4,779 | LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems | 2021 | SIGMOD | 5.9259373e-05 |
| 6,061 | Optimizing Machine Learning Workloads in Collaborative Environments | 2020 | SIGMOD | 5.2270653e-05 |
| 6,379 | SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments | 2024 | VLDB | 5.0844374e-05 |
| 7,656 | Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets | 2022 | SIGMOD | 4.6826896e-05 |
| 8,183 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 4.5615358e-05 |
| 8,531 | Sommelier: Curating DNN Models for the Masses | 2022 | SIGMOD | 4.4893996e-05 |
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