| Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout S Horvath, S Laskaridis, M Almeida, I Leontiadis, S Venieris, N Lane Advances in Neural Information Processing Systems 34, 12876-12889, 2021 | 401 | 2021 |
| SPINN: Synergistic progressive inference of neural networks over device and cloud S Laskaridis, SI Venieris, M Almeida, I Leontiadis, ND Lane Proceedings of the 26th annual international conference on mobile computing …, 2020 | 400 | 2020 |
| Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions S Laskaridis, A Kouris, ND Lane Proceedings of the 5th International Workshop on Embedded and Mobile Deep …, 2021 | 196 | 2021 |
| EmBench: Quantifying performance variations of deep neural networks across modern commodity devices M Almeida, S Laskaridis, I Leontiadis, SI Venieris, ND Lane The 3rd international workshop on deep learning for mobile systems and …, 2019 | 90 | 2019 |
| Smart at what cost? characterising mobile deep neural networks in the wild M Almeida, S Laskaridis, A Mehrotra, L Dudziak, I Leontiadis, ND Lane Proceedings of the 21st ACM Internet Measurement Conference, 658-672, 2021 | 56 | 2021 |
| HAPI: Hardware-aware progressive inference S Laskaridis, SI Venieris, H Kim, ND Lane Proceedings of the 39th International Conference on Computer-Aided Design, 1-9, 2020 | 56 | 2020 |
| Multi-exit semantic segmentation networks A Kouris, SI Venieris, S Laskaridis, N Lane European Conference on Computer Vision, 330-349, 2022 | 51 | 2022 |
| Dyno: Dynamic onloading of deep neural networks from cloud to device M Almeida, S Laskaridis, SI Venieris, I Leontiadis, ND Lane ACM Transactions on Embedded Computing Systems 21 (6), 1-24, 2022 | 48 | 2022 |
| MELTing point: Mobile Evaluation of Language Transformers S Laskaridis, K Katevas, L Minto, H Haddadi International Conference on Mobile Computing and Networking (MobiCom'24), 2024 | 44 | 2024 |
| It's always personal: Using early exits for efficient on-device CNN personalisation I Leontiadis, S Laskaridis, SI Venieris, ND Lane Proceedings of the 22nd International Workshop on Mobile Computing Systems …, 2021 | 38 | 2021 |
| Shrinkml: End-to-end asr model compression using reinforcement learning Ł Dudziak, MS Abdelfattah, R Vipperla, S Laskaridis, ND Lane INTERSPEECH 2019, 2019 | 31* | 2019 |
| The future of consumer edge-ai computing S Laskaridis, SI Venieris, A Kouris, R Li, ND Lane IEEE Pervasive Computing, 2024 | 21 | 2024 |
| Fluid batching: Exit-aware preemptive serving of early-exit neural networks on edge npus A Kouris, SI Venieris, S Laskaridis, ND Lane International Conference on Computer-Aided Design (ICCAD'23), 2022 | 17 | 2022 |
| Recurrent Early Exits for Federated Learning with Heterogeneous Clients R Lee, J Fernandez-Marques, SX Hu, D Li, S Laskaridis, Ł Dudziak, ... International Conference on Machine Learning (ICML'24), 2024 | 12 | 2024 |
| Fedoras: Federated architecture search under system heterogeneity L Dudziak, S Laskaridis, J Fernandez-Marques Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022 | 12 | 2022 |
| Maestro: Uncovering Low-Rank Structures via Trainable Decomposition S Horváth, S Laskaridis, S Rajput, H Wang International Conference on Machine Learning (ICML'24), 2023 | 9 | 2023 |
| Federated learning for inference at anytime and anywhere Z Liu, D Li, J Fernandez-Marques, S Laskaridis, Y Gao, SZ Li, SX Hu, ... arXiv preprint arXiv:2212.04084, 2022 | 9 | 2022 |
| Mobile and edge evaluation of large language models S Laskaridis, K Katevas, L Minto, H Haddadi Workshop on Efficient Systems for Foundation Models II@ ICML2024, 0 | 8 | |
| Method and system for implementing a variable accuracy neural network S LASKARIDIS, H Kim, S VENIERIS US Patent App. 16/923,447, 2021 | 7 | 2021 |
| Federated mobile sensing for activity recognition S Laskaridis, D Spathis, M Almeida Proceedings of the 27th Annual International Conference on Mobile Computing …, 2021 | 6 | 2021 |
Nicholas LaneUniversity of Cambridge and Flower LabsVerified email at cam.ac.uk
