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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Huayi Zhang (Worcester Polytechnic Institute)
- 2. Binwei Yan (Massachusetts Institute of Technology)
- 3. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 4. Samuel Madden (Massachusetts Institute of Technology)
- 5. Elke Rundensteiner (Worcester Polytechnic Institute)
BibTeX Citation
@article{zhang_vldb24,
title = {{MetaStore: Analyzing Deep Learning Meta-Data at Scale}},
author = {Zhang, Huayi and Yan, Binwei and Cao, Lei and Madden, Samuel and Rundensteiner, Elke},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {6},
pages = {1446--1459},
doi = {10.14778/3648160.3648182},
url = {https://doi.org/10.14778/3648160.3648182},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,670 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 0.00010045615 |
| 2,584 | Complaint-driven Training Data Debugging for Query 2.0 | 2020 | SIGMOD | 8.3783546e-05 |
| 4,582 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.6193306e-05 |
| 6,079 | FACET: Robust Counterfactual Explanation Analytics | 2023 | SIGMOD | 5.9850223e-05 |
| 6,960 | DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation | 2022 | VLDB | 5.7303405e-05 |
| 8,876 | LANCET: Labeling Complex Data at Scale | 2021 | VLDB | 5.3534114e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,520 | Experimental Analysis of Large-scale Learnable Vector Storage Compression | 2024 | VLDB |
| 2 | 5,925 | Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems | 2021 | VLDB |
| 3 | 10,584 | QStore: Quantization-Aware Compressed Model Storage | 2026 | VLDB |
| 4 | 1,446 | Analyzing and Mitigating Data Stalls in DNN Training | 2021 | VLDB |
| 5 | 9,219 | MemFlow: Memory-Aware Distributed Deep Learning | 2020 | SIGMOD |
| 6 | 11,542 | Simplifying Access to Large-scale Structured Datasets by Meta-Profiling with Scalable Training Set Enrichment | 2022 | SIGMOD |
| 7 | 5,901 | A Scalable AutoML Approach Based on Graph Neural Networks | 2022 | VLDB |
| 8 | 6,708 | Serving Deep Learning Models with Deduplication from Relational Databases | 2022 | VLDB |
| 9 | 8,908 | Privacy and Accuracy-Aware AI/ML Model Deduplication | 2025 | SIGMOD |
| 10 | 10,386 | NeurStore: Efficient In-database Deep Learning Model Management System | 2026 | SIGMOD |