| Driver behavior recognition via interwoven deep convolutional neural nets with multi-stream inputs C Zhang, R Li, W Kim, D Yoon, P Patras Ieee Access 8, 191138-191151, 2020 | 54 | 2020 |
| Network intrusion detection based on conditional Wasserstein generative adversarial network and cost-sensitive stacked autoencoder G Zhang, X Wang, R Li, Y Song, J He, J Lai IEEE access 8, 190431-190447, 2020 | 39 | 2020 |
| Max-min fair resource allocation in millimetre-wave backhauls R Li, P Patras IEEE Transactions on Mobile Computing 19 (8), 1879-1895, 2019 | 14* | 2019 |
| Transport protocols behaviour study in evolving mobile networks R Li, M Shariat, M Nekovee 2016 International Symposium on Wireless Communication Systems (ISWCS), 456-460, 2016 | 12 | 2016 |
| Learning from experience: Efficient decentralized scheduling for 60GHz mesh networks GH Sim, R Li, C Cano, D Malone, P Patras, J Widmer 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and …, 2016 | 12 | 2016 |
| TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge YD Kwon, R Li, S Venieris, J Chauhan, ND Lane, C Mascolo Forty-first International Conference on Machine Learning, 2024 | 10* | 2024 |
| Learning driven mobility control of airborne base stations in emergency networks R Li, C Zhang, P Patras, R Stanica, F Valois ACM SIGMETRICS Performance Evaluation Review 46 (3), 163-166, 2019 | 9 | 2019 |
| A channel coding benchmark for meta-learning R Li, O Bohdal, R Mishra, H Kim, D Li, N Lane, T Hospedales arXiv preprint arXiv:2107.07579, 2021 | 8 | 2021 |
| Learning to listen... on-device: Present and future perspectives of on-device asr R Vipperla, S Ishtiaq, R Li, S Bhattacharya, I Leontiadis, ND Lane GetMobile: Mobile Computing and Communications 23 (4), 5-9, 2020 | 8 | 2020 |
| The future of consumer edge-ai computing S Laskaridis, SI Venieris, A Kouris, R Li, ND Lane arXiv preprint arXiv:2210.10514, 2022 | 5 | 2022 |
| Delmu: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls R Li, C Zhang, P Cao, P Patras, JS Thompson Machine Learning for Networking: First International Conference, MLN 2018 …, 2019 | 5 | 2019 |
| TransMUSE: Transferable traffic prediction in multi-service edge networks L Xu, H Liu, J Song, R Li, Y Hu, X Zhou, P Patras Computer Networks 221, 109518, 2023 | 4 | 2023 |
| Meta-learned kernel for blind super-resolution kernel estimation R Lee, R Li, S Venieris, T Hospedales, F Huszár, ND Lane Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 3 | 2024 |
| Inferring facing direction from voice signals YL Wei, R Li, A Mehrotra, RR Choudhury, N Lane arXiv preprint arXiv:2109.13094, 2021 | 3 | 2021 |
| Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference W Luk, KFC Yiu, R Li, K Mishchenko, SI Venieris, H Fan arXiv preprint arXiv:2405.18628, 2024 | | 2024 |
| Resource management in future mobile networks: from millimetre-wave backhauls to airborne access networks R Li The University of Edinburgh, 2019 | | 2019 |
| Federated Learning Under Second-Order Data Heterogeneity K Mishchenko, R Li, H Fan, S Venieris | | |
Paul PatrasNet AI / The University of Edinburgh在 ieee.org 的电子邮件经过验证
