Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets
Summary: Frames DTL adaptation with frozen pre-trained layers as a multi-query optimization problem and introduces two optimizations to share computations across model selections on evolving labeled data. Implemented in Nautilus, it yields up to 5x speedup over naive re-training on benchmark workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Supun Nakandala
- 2. Arun Kumar
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,885 | Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines | 2023 | VLDB | 4.8908367e-05 |
| 9,787 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 4.2799988e-05 |
| 10,479 | Alsatian: Optimizing Model Search for Deep Transfer Learning | 2025 | SIGMOD | 4.1905499e-05 |
| 10,979 | StarfishDB: a Query Execution Engine for Relational Probabilistic Programming | 2024 | SIGMOD | 4.1905499e-05 |
| 13,185 | Reimagining Deep Learning Systems Through the Lens of Data Systems | 2024 | VLDB | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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