MetaStore: Analyzing Deep Learning Meta-Data at Scale
Summary: MetaStore stores compact backprop intermediates—prefix and suffix gradients—that exactly reconstruct full model gradients, drastically reducing gradient size. It runs gradient analytics directly on these compact structures, achieving 4–678× storage and 2–1000× runtime gains on VGG/BERT/ResNet. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Huayi Zhang
- 2. Binwei Yan
- 3. Lei Cao
- 4. Samuel Madden
- 5. Elke Rundensteiner
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,152 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 9.4239787e-05 |
| 2,753 | Complaint-driven Training Data Debugging for Query 2.0 | 2020 | SIGMOD | 8.1724339e-05 |
| 4,554 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.0911296e-05 |
| 5,997 | FACET: Robust Counterfactual Explanation Analytics | 2023 | SIGMOD | 5.2415551e-05 |
| 6,000 | DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation | 2022 | VLDB | 5.2415551e-05 |
| 8,714 | LANCET: Labeling Complex Data at Scale | 2021 | VLDB | 4.4619818e-05 |
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