MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors. - Royson Lee; Stylianos I. Venieris; Lukasz Dudziak; Sourav Bhattacharya; Nicholas D. Lane (MobiCom, 2019)

Poster: MobiSR - Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors. - Royson Lee; Stylianos I. Venieris; Lukasz Dudziak; Sourav Bhattacharya; Nicholas D. Lane (MobiCom, 2019)

Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions. - Royson Lee; Stylianos I. Venieris; Nicholas D. Lane (DistributedML@CoNEXT, 2020)

Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator. - Mohamed S. Abdelfattah; Lukasz Dudziak; Thomas Chau 001; Royson Lee; Hyeji Kim; Nicholas D. Lane (DAC, 2020)

Journey Towards Tiny Perceptual Super-Resolution. - Royson Lee; Lukasz Dudziak; Mohamed S. Abdelfattah; Stylianos I. Venieris; Hyeji Kim; Hongkai Wen 001; Nicholas D. Lane (ECCV (26), 2020)

Codesign-NAS: Automatic FPGA/CNN Codesign Using Neural Architecture Search. - Mohamed S. Abdelfattah; Lukasz Dudziak; Thomas Chau 001; Royson Lee; Hyeji Kim; Nicholas D. Lane (FPGA, 2020)

BRP-NAS: Prediction-based NAS using GCNs. - Lukasz Dudziak; Thomas Chau 001; Mohamed S. Abdelfattah; Royson Lee; Hyeji Kim; Nicholas D. Lane (NeurIPS, 2020)

Temporal Kernel Consistency for Blind Video Super-Resolution. - Lichuan Xiang; Royson Lee; Mohamed S. Abdelfattah; Nicholas D. Lane; Hongkai Wen 001 (ICCVW, 2021)

Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design. - Hongxiang Fan; Thomas Chau 001; Stylianos I. Venieris; Royson Lee; Alexandros Kouris; Wayne Luk; Nicholas D. Lane; Mohamed S. Abdelfattah (MICRO, 2022)

Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions. - Royson Lee; Stylianos I. Venieris; Nicholas D. Lane (ACM Comput. Surv., 2022)

FedL2P: Federated Learning to Personalize. - Royson Lee; Minyoung Kim 001; Da Li 001; Xinchi Qiu; Timothy M. Hospedales; Ferenc Huszar; Nicholas D. Lane (NeurIPS, 2023)

How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor. - Hrushikesh Loya; Lukasz Dudziak; Abhinav Mehrotra; Royson Lee; Javier Fernández-Marqués; Nicholas D. Lane; Hongkai Wen 001 (CoRR, 2023)

MobileQuant: Mobile-friendly Quantization for On-device Language Models. - Fuwen Tan; Royson Lee; Lukasz Dudziak; Shell Xu Hu; Sourav Bhattacharya; Timothy M. Hospedales; Georgios Tzimiropoulos; Brais Martínez (EMNLP (Findings), 2024)

Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. - Wanru Zhao; Yihong Chen; Royson Lee; Xinchi Qiu; Yan Gao 016; Hongxiang Fan; Nicholas Donald Lane (ICLR, 2024)

Recurrent Early Exits for Federated Learning with Heterogeneous Clients. - Royson Lee; Javier Fernández-Marqués; Shell Xu Hu; Da Li 001; Stefanos Laskaridis; Lukasz Dudziak; Timothy M. Hospedales; Ferenc Huszár; Nicholas Donald Lane (ICML, 2024)

A Bayesian Approach to Data Point Selection. - Xinnuo Xu; Minyoung Kim 001; Royson Lee; Brais Martínez; Timothy M. Hospedales (NeurIPS, 2024)

Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. - Royson Lee; Rui Li 052; Stylianos I. Venieris; Timothy M. Hospedales; Ferenc Huszár; Nicholas D. Lane (WACV, 2024)

Photon: Federated LLM Pre-Training. - Lorenzo Sani; Alex Iacob; Zeyu Cao; Royson Lee; Bill Marino; Yan Gao 016; Dongqi Cai 001; Zexi Li; Wanru Zhao; Xinchi Qiu; Nicholas D. Lane (CoRR, 2024)

NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution. - Stylianos I. Venieris; Mário Almeida; Royson Lee; Nicholas D. Lane (IEEE Trans. Mob. Comput., 2024)

Progressive Mixed-Precision Decoding for Efficient LLM Inference. - Hao Mark Chen; Fuwen Tan; Alexandros Kouris; Royson Lee; Hongxiang Fan; Stylianos I. Venieris (ICLR, 2025)

FedP2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs. - Royson Lee; Minyoung Kim 001; Fady Rezk; Rui Li 052; Stylianos I. Venieris; Timothy M. Hospedales (CoRR, 2025)

Model Diffusion for Certifiable Few-shot Transfer Learning. - Fady Rezk; Royson Lee; Henry Gouk; Timothy M. Hospedales; Minyoung Kim 001 (CoRR, 2025)