QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models
Summary: QCore enables continual, on-device calibration of quantized models under distribution shift. It maintains a compact, evolving data core and uses a lightweight bit-flipping network instead of backpropagation, preserving past knowledge with low storage and compute. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. David Campos (Aalborg University)
- 2. Bin Yang (East China Normal University)
- 3. Tung Kieu (Aalborg University)
- 4. Miao Zhang (Harbin Engineering University)
- 5. Chenjuan Guo (East China Normal University)
- 6. Christian S. Jensen (Aalborg University)
BibTeX Citation
@article{campos_vldb24,
title = {{QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models}},
author = {Campos, David and Yang, Bin and Kieu, Tung and Zhang, Miao and Guo, Chenjuan and Jensen, Christian S.},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {2708--2721},
doi = {10.14778/3681954.3681957},
url = {https://doi.org/10.14778/3681954.3681957},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,369 | TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods | 2024 | VLDB | 7.4706661e-05 |
| 10,808 | Noise Matters: Cross Contrastive Learning for Flink Anomaly Detection | 2025 | VLDB | 5.093636e-05 |
| 10,861 | Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching | 2025 | VLDB | 5.093636e-05 |
| 10,883 | TEAM: Topological Evolution-aware Framework for Traffic Forecasting | 2025 | VLDB | 5.093636e-05 |
| 13,318 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB | - |
| 13,320 | A Memory Guided Transformer for Time Series Forecasting | 2025 | VLDB | - |
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