Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach
- DOI
- 10.1109/access.2025.3578009
- Published
- 2025
- Container
- IEEE Access
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1109/access.2025.3578009,
title = {Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach},
author = {Haneul Ko and Sangwon Seo and Sangheon Pack},
year = {2025},
journal = {IEEE Access},
doi = {10.1109/access.2025.3578009},
url = {https://doi.org/10.1109/access.2025.3578009}
}RIS
TY - JOUR TI - Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach AU - Haneul Ko AU - Sangwon Seo AU - Sangheon Pack PY - 2025 JO - IEEE Access DO - 10.1109/access.2025.3578009 UR - https://doi.org/10.1109/access.2025.3578009 ER -
APA
Ko, H., Seo, S., & Pack, S. (2025). Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach. IEEE Access. https://doi.org/10.1109/access.2025.3578009
Source records
- doaj · retrieved 2026-09-24T21:04:59.372Z