DroneBandit: Multi-armed contextual bandits for collaborative edge-to-cloud inference in resource-constrained nanodrones

Author:

Chacun Guillaume1ORCID,Akeddar Mehdi1ORCID,Rieder Thomas1ORCID,Da Rocha Carvalho Bruno1ORCID,Zapater Marina1ORCID

Affiliation:

1. REDS, School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, Switzerland

Publisher

ACM

Reference16 articles.

1. D. Bouneffouf and I. Rish. 2019. A Survey on Practical Applications of Multi-Armed and Contextual Bandits. CoRR abs/1904.10040 (2019). http://arxiv.org/abs/1904.10040

2. E.D. Cubuk B. Zoph J. Shlens and Q.V. Le. 2019. RandAugment: Practical data augmentation with no separate search. CoRR abs/1909.13719 (2019). http://arxiv.org/abs/1909.13719

3. Understanding the difficulty of training deep feedforward neural networks;Glorot X.;Journal of Machine Learning Research - Proceedings Track,2010

4. Obstacle detection of rail transit based on deep learning

5. D. Hendrycks N. Mu E.D. Cubuk B. Zoph J. Gilmer and B. Lakshminarayanan. 2020. AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. arXiv:1912.02781.

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