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ORCIDNicholas D. Lane
Nicholas Donald Lane
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- affiliation: University of Cambridge, UK
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2020 – today
- 2024
[j39]Stylianos I. Venieris
, Mário Almeida, Royson Lee, Nicholas D. Lane:
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution. IEEE Trans. Mob. Comput. 23(3): 2367-2381 (2024)- [c146]Wanru Zhao, Yihong Chen, Royson Lee, Xinchi Qiu, Yan Gao, Hongxiang Fan, Nicholas Donald Lane:
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. ICLR 2024 - [c145]Young D. Kwon, Rui Li, Stylianos I. Venieris, Jagmohan Chauhan, Nicholas Donald Lane, Cecilia Mascolo:
TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge. ICML 2024 - [c144]Royson Lee, Javier Fernández-Marqués, Shell Xu Hu, Da Li, Stefanos Laskaridis, Lukasz Dudziak, Timothy M. Hospedales, Ferenc Huszár, Nicholas Donald Lane:
Recurrent Early Exits for Federated Learning with Heterogeneous Clients. ICML 2024 - [c143]Lichuan Xiang, Lukasz Dudziak, Mohamed S. Abdelfattah, Abhinav Mehrotra, Nicholas Donald Lane, Hongkai Wen:
Towards Neural Architecture Search through Hierarchical Generative Modeling. ICML 2024 - [c142]Royson Lee, Rui Li, Stylianos I. Venieris, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. WACV 2024: 1485-1494 - [e7]Weisong Shi, Deepak Ganesan, Nicholas D. Lane:
Proceedings of the 30th Annual International Conference on Mobile Computing and Networking, ACM MobiCom 2024, Washington D.C., DC, USA, November 18-22, 2024. ACM 2024 [contents] - [i101]Herbert Woisetschläger, Alexander Erben, Bill Marino, Shiqiang Wang, Nicholas D. Lane, Ruben Mayer, Hans-Arno Jacobsen:
Federated Learning Priorities Under the European Union Artificial Intelligence Act. CoRR abs/2402.05968 (2024) - [i100]Xinchi Qiu, Yan Gao, Lorenzo Sani, Heng Pan, Wanru Zhao, Pedro P. B. de Gusmao, Mina Alibeigi, Alex Iacob, Nicholas D. Lane:
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients. CoRR abs/2402.10191 (2024) - [i99]Wanru Zhao, Yaxin Du, Nicholas Donald Lane, Siheng Chen, Yanfeng Wang:
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models. CoRR abs/2403.04529 (2024) - [i98]Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner:
Aardvark Weather: end-to-end data-driven weather forecasting. CoRR abs/2404.00411 (2024) - [i97]Wanru Zhao, Vidit Khazanchi, Haodi Xing, Xuanli He, Qiongkai Xu, Nicholas Donald Lane:
Attacks on Third-Party APIs of Large Language Models. CoRR abs/2404.16891 (2024) - [i96]Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino, Yan Gao, Tomás Paulik, Wanru Zhao, William F. Shen, Preslav Aleksandrov, Xinchi Qiu, Nicholas D. Lane:
The Future of Large Language Model Pre-training is Federated. CoRR abs/2405.10853 (2024) - [i95]Alex Iacob, Lorenzo Sani, Bill Marino, Preslav Aleksandrov, William F. Shen, Nicholas Donald Lane:
Worldwide Federated Training of Language Models. CoRR abs/2405.14446 (2024) - [i94]Royson Lee, Javier Fernández-Marqués, Shell Xu Hu, Da Li, Stefanos Laskaridis, Lukasz Dudziak, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
Recurrent Early Exits for Federated Learning with Heterogeneous Clients. CoRR abs/2405.14791 (2024) - [i93]Bao Nguyen, Lorenzo Sani, Xinchi Qiu, Pietro Liò, Nicholas D. Lane:
Sheaf HyperNetworks for Personalized Federated Learning. CoRR abs/2405.20882 (2024) - [i92]Bill Marino, Preslav Aleksandrov, Carwyn Rahman, Yulu Pi, Bill Shen, Rui-jie Yew, Nicholas D. Lane:
Compliance Cards: Computational Artifacts for Automated AI Regulation Compliance. CoRR abs/2406.14758 (2024) - [i91]Xinchi Qiu, William F. Shen, Yihong Chen, Nicola Cancedda, Pontus Stenetorp, Nicholas D. Lane:
PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs. CoRR abs/2406.16810 (2024) - [i90]Holger R. Roth, Daniel J. Beutel, Yan Cheng, Javier Fernández-Marqués, Heng Pan, Chester Chen, Zhihong Zhang, Yuhong Wen, Sean Yang, Isaac Yang, Yuan-Ting Hsieh, Ziyue Xu, Daguang Xu, Nicholas D. Lane, Andrew Feng:
Supercharging Federated Learning with Flower and NVIDIA FLARE. CoRR abs/2407.00031 (2024) - 2023
- [j38]Stylianos I. Venieris, Christos-Savvas Bouganis, Nicholas D. Lane:
Multiple-Deep Neural Network Accelerators for Next-Generation Artificial Intelligence Systems. Computer 56(3): 70-79 (2023) - [j37]Xinchi Qiu, Titouan Parcollet, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane:
A First Look into the Carbon Footprint of Federated Learning. J. Mach. Learn. Res. 24: 129:1-129:23 (2023) - [j36]Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Anna Wuest, Sarthak Pati, Hasan Kassem, Maximilian Zenk, Ujjwal Baid, Prakash Narayana Moorthy, Alexander Chowdhury, Junyi Guo, Sahil S. Nalawade, Jacob Rosenthal, David Kanter, Maria Xenochristou, Daniel J. Beutel, Verena Chung, Timothy Bergquist, James A. Eddy, Abubakar Abid, Lewis Tunstall, Omar Sanseviero, Dimitrios Dimitriadis, Yiming Qian, Xinxing Xu, Yong Liu, Rick Siow Mong Goh, Srini Bala, Victor Bittorf, Sreekar Reddy Puchala, Biagio Ricciuti, Soujanya Samineni, Eshna Sengupta, Akshay Chaudhari, Cody Coleman, Bala Desinghu, Gregory F. Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Xinyuan Huang, Satyananda Kashyap, Nicholas D. Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Cassiano Ferro Moraes, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G. Anthony Reina, Pablo Ribalta, Abhishek Singh, Jayaraman J. Thiagarajan, Jacob Albrecht, Thomas Wolf, Geralyn Miller, Huazhu Fu, Prashant Shah, Daguang Xu, Poonam Yadav, David Talby, Mark M. Awad, Jeremy P. Howard, Michael Rosenthal, Luigi Marchionni, Massimo Loda, Jason M. Johnson, Spyridon Bakas, Peter Mattson:
Federated benchmarking of medical artificial intelligence with MedPerf. Nat. Mac. Intell. 5(7): 799-810 (2023) - [j35]Maximilian Kapsecker, Daniel N. Nugraha, Christoph Weinhuber, Nicholas D. Lane, Stephan M. Jonas:
Federated Learning with Swift: An Extension of Flower and Performance Evaluation. SoftwareX 24: 101533 (2023) - [j34]Stylianos I. Venieris, Javier Fernández-Marqués, Nicholas D. Lane:
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation. ACM Trans. Design Autom. Electr. Syst. 28(6): 92:1-92:31 (2023) - [c141]Lichuan Xiang, Lukasz Dudziak, Mohamed S. Abdelfattah, Thomas C. P. Chau, Nicholas D. Lane, Hongkai Wen:
Zero-Cost Operation Scoring in Differentiable Architecture Search. AAAI 2023: 10453-10463 - [c140]Chenyang Ma, Xinchi Qiu, Daniel J. Beutel, Nicholas D. Lane:
Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates. EuroMLSys@EuroSys 2023: 56-63 - [c139]Alex Iacob, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Can Fair Federated Learning Reduce the need for Personalisation? EuroMLSys@EuroSys 2023: 131-139 - [c138]Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão, Mina Alibeigi, Jiajun Shen, Nicholas D. Lane:
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning. ICCV 2023: 16418-16427 - [c137]Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris, Nicholas D. Lane:
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads. MICRO 2023: 353-366 - [c136]Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszar, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. NeurIPS 2023 - [c135]Chongyang Wang, Yuan Gao, Chenyou Fan, Junjie Hu, Tin Lum Lam, Nicholas D. Lane, Nadia Bianchi-Berthouze:
Learn2Agree: Fitting with Multiple Annotators Without Objective Ground Truth. TML4H 2023: 147-162 - [c134]Viktor Valadi, Xinchi Qiu, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Mina Alibeigi:
FedVal: Different good or different bad in federated learning. USENIX Security Symposium 2023: 6365-6380 - [c133]Yadong Mao, Zhuqi Xiao, Che-Tsung Lin, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Christopher Zach, Mina Alibeigi:
Decentralized Training of 3D Lane Detection with Automatic Labeling Using HD Maps. VTC2023-Spring 2023: 1-7 - [c132]Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lió:
A Federated Learning Benchmark for Drug-Target Interaction. WWW (Companion Volume) 2023: 1177-1181 - [i89]Filip Svoboda, Gianluca Mittone, Nicholas D. Lane, Pietro Liò:
A Federated Learning Benchmark for Drug-Target Interaction. CoRR abs/2302.07684 (2023) - [i88]Chenyang Ma, Xinchi Qiu, Daniel J. Beutel, Nicholas D. Lane:
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates. CoRR abs/2304.07537 (2023) - [i87]Alex Iacob, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Can Fair Federated Learning reduce the need for Personalisation? CoRR abs/2305.02728 (2023) - [i86]Xinchi Qiu, Heng Pan, Wanru Zhao, Chenyang Ma, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Efficient Vertical Federated Learning with Secure Aggregation. CoRR abs/2305.11236 (2023) - [i85]Alex Iacob, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Armand K. Koupai, Mohammud Junaid Bocus, Raúl Santos-Rodríguez, Robert J. Piechocki, Ryan McConville:
Privacy in Multimodal Federated Human Activity Recognition. CoRR abs/2305.12134 (2023) - [i84]Xinchi Qiu, Heng Pan, Wanru Zhao, Chenyang Ma, Pedro P. B. de Gusmao, Nicholas D. Lane:
vFedSec: Efficient Secure Aggregation for Vertical Federated Learning via Secure Layer. CoRR abs/2305.16794 (2023) - [i83]Javier Fernández-Marqués, Ahmed F. AbouElhamayed, Nicholas D. Lane, Mohamed S. Abdelfattah:
Are We There Yet? Product Quantization and its Hardware Acceleration. CoRR abs/2305.18334 (2023) - [i82]Viktor Valadi, Xinchi Qiu, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Mina Alibeigi:
FedVal: Different good or different bad in federated learning. CoRR abs/2306.04040 (2023) - [i81]Lorenzo Sani, Pedro Porto Buarque de Gusmão, Alex Iacob, Wanru Zhao, Xinchi Qiu, Yan Gao, Javier Fernández-Marqués, Nicholas Donald Lane:
High-throughput Simulation of Federated Learning via Resource-Aware Client Placement. CoRR abs/2306.17453 (2023) - [i80]Lekang Jiang, Filip Svoboda, Nicholas D. Lane:
FDAPT: Federated Domain-adaptive Pre-training for Language Models. CoRR abs/2307.06933 (2023) - [i79]Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão, Mina Alibeigi, Jiajun Shen, Nicholas D. Lane:
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning. CoRR abs/2307.07393 (2023) - [i78]Young D. Kwon, Rui Li, Stylianos I. Venieris, Jagmohan Chauhan, Nicholas D. Lane, Cecilia Mascolo:
TinyTrain: Deep Neural Network Training at the Extreme Edge. CoRR abs/2307.09988 (2023) - [i77]Stylianos I. Venieris, Javier Fernández-Marqués, Nicholas D. Lane:
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation. CoRR abs/2307.13412 (2023) - [i76]Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. CoRR abs/2310.02420 (2023) - [i75]Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris, Nicholas D. Lane:
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads. CoRR abs/2310.11096 (2023) - [i74]Hrushikesh Loya, Lukasz Dudziak, Abhinav Mehrotra, Royson Lee, Javier Fernández-Marqués, Nicholas D. Lane, Hongkai Wen:
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor. CoRR abs/2311.18451 (2023) - 2022
- [j33]Royson Lee, Stylianos I. Venieris, Nicholas D. Lane:
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions. ACM Comput. Surv. 54(8): 169:1-169:30 (2022) - [j32]Qingqing Cao, Prerna Khanna, Nicholas D. Lane, Aruna Balasubramanian:
MobiVQA: Efficient On-Device Visual Question Answering. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(2): 44:1-44:23 (2022) - [j31]Edgar Liberis, Nicholas D. Lane:
Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(4): 171:1-171:19 (2022) - [j30]Mário Almeida, Stefanos Laskaridis, Stylianos I. Venieris, Ilias Leontiadis, Nicholas D. Lane:
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device. ACM Trans. Embed. Comput. Syst. 21(6): 71:1-71:24 (2022) - [j29]Danyang Li, Jingao Xu, Zheng Yang, Chenshu Wu, Jianbo Li, Nicholas D. Lane:
Wireless Localization with Spatial-Temporal Robust Fingerprints. ACM Trans. Sens. Networks 18(1): 15:1-15:23 (2022) - [j28]Shaoduo Gan, Akhil Mathur, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane:
FRuDA: Framework for Distributed Adversarial Domain Adaptation. IEEE Trans. Parallel Distributed Syst. 33(11): 3153-3164 (2022) - [c131]Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis, Nicholas D. Lane:
Multi-Exit Semantic Segmentation Networks. ECCV (21) 2022: 330-349 - [c130]Yasar Abbas Ur Rehman, Yan Gao, Jiajun Shen, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Federated Self-supervised Learning for Video Understanding. ECCV (31) 2022: 506-522 - [c129]Filip Svoboda, Javier Fernández-Marqués, Edgar Liberis, Nicholas D. Lane:
Deep learning on microcontrollers: a study on deployment costs and challenges. EuroMLSys@EuroSys 2022: 54-63 - [c128]Wanru Zhao, Xinchi Qiu, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Nicholas D. Lane:
Protea: client profiling within federated systems using flower. FedEdge@MobiCom 2022: 1-6 - [c127]Yan Gao, Titouan Parcollet, Salah Zaiem, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Daniel J. Beutel, Nicholas D. Lane:
End-to-End Speech Recognition from Federated Acoustic Models. ICASSP 2022: 7227-7231 - [c126]Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal:
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. ICLR 2022 - [c125]Xinchi Qiu, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Yan Gao, Titouan Parcollet, Nicholas Donald Lane:
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. ICLR 2022 - [c124]Alberto Gil Couto Pimentel Ramos, Abhinav Mehrotra, Nicholas Donald Lane, Sourav Bhattacharya:
Conditioning Sequence-to-sequence Networks with Learned Activations. ICLR 2022 - [c123]Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas Donald Lane:
Do We Need Anisotropic Graph Neural Networks? ICLR 2022 - [c122]Yan Gao, Javier Fernández-Marqués, Titouan Parcollet, Abhinav Mehrotra, Nicholas D. Lane:
Federated Self-supervised Speech Representations: Are We There Yet? INTERSPEECH 2022: 3809-3813 - [c121]Hongxiang Fan, Thomas Chau, Stylianos I. Venieris, Royson Lee, Alexandros Kouris, Wayne Luk, Nicholas D. Lane, Mohamed S. Abdelfattah:
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design. MICRO 2022: 599-615 - [c120]Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis, Nicholas D. Lane:
Adaptable mobile vision systems through multi-exit neural networks. MobiSys 2022: 575-576 - [c119]Thomas Chau, Lukasz Dudziak, Hongkai Wen, Nicholas D. Lane, Mohamed S. Abdelfattah:
BLOX: Macro Neural Architecture Search Benchmark and Algorithms. NeurIPS 2022 - [c118]Yan Gao, Javier Fernández-Marqués, Titouan Parcollet, Pedro P. B. de Gusmao, Nicholas D. Lane:
Match to Win: Analysing Sequences Lengths for Efficient Self-Supervised Learning in Speech and Audio. SLT 2022: 115-122 - [i73]Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal:
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. CoRR abs/2202.08132 (2022) - [i72]Yan Gao, Javier Fernández-Marqués, Titouan Parcollet, Abhinav Mehrotra, Nicholas D. Lane:
Federated Self-supervised Speech Representations: Are We There Yet? CoRR abs/2204.02804 (2022) - [i71]Kwing Hei Li, Pedro Porto Buarque de Gusmão, Daniel J. Beutel, Nicholas D. Lane:
Secure Aggregation for Federated Learning in Flower. CoRR abs/2205.06117 (2022) - [i70]Stylianos I. Venieris, Christos-Savvas Bouganis, Nicholas D. Lane:
Multi-DNN Accelerators for Next-Generation AI Systems. CoRR abs/2205.09376 (2022) - [i69]Wanru Zhao, Xinchi Qiu, Javier Fernández-Marqués, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Protea: Client Profiling within Federated Systems using Flower. CoRR abs/2207.01053 (2022) - [i68]Yasar Abbas Ur Rehman, Yan Gao, Jiajun Shen, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Federated Self-supervised Learning for Video Understanding. CoRR abs/2207.01975 (2022) - [i67]Xinchi Qiu, Javier Fernández-Marqués, Pedro Porto Buarque de Gusmão, Yan Gao, Titouan Parcollet, Nicholas Donald Lane:
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. CoRR abs/2208.02507 (2022) - [i66]Hongxiang Fan, Thomas Chun-Pong Chau, Stylianos I. Venieris, Royson Lee, Alexandros Kouris, Wayne Luk, Nicholas D. Lane, Mohamed S. Abdelfattah:
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design. CoRR abs/2209.09570 (2022) - [i65]Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis, Nicholas D. Lane:
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs. CoRR abs/2209.13443 (2022) - [i64]Yan Gao, Javier Fernández-Marqués, Titouan Parcollet, Pedro P. B. de Gusmao, Nicholas D. Lane:
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio. CoRR abs/2209.15575 (2022) - [i63]Thomas Chun-Pong Chau, Lukasz Dudziak, Hongkai Wen, Nicholas Donald Lane, Mohamed S. Abdelfattah:
BLOX: Macro Neural Architecture Search Benchmark and Algorithms. CoRR abs/2210.07271 (2022) - [i62]Stefanos Laskaridis, Stylianos I. Venieris, Alexandros Kouris, Rui Li, Nicholas D. Lane:
The Future of Consumer Edge-AI Computing. CoRR abs/2210.10514 (2022) - [i61]Edgar Liberis, Nicholas D. Lane:
Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution. CoRR abs/2211.17246 (2022) - [i60]Royson Lee, Rui Li, Stylianos I. Venieris, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. CoRR abs/2212.07886 (2022) - [i59]Stylianos I. Venieris, Mário Almeida, Royson Lee, Nicholas D. Lane:
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution. CoRR abs/2212.09501 (2022) - 2021
- [j27]Chongyang Wang, Temitayo A. Olugbade, Akhil Mathur, Amanda C. de C. Williams, Nicholas D. Lane, Nadia Bianchi-Berthouze:
Chronic Pain Protective Behavior Detection with Deep Learning. ACM Trans. Comput. Heal. 2(3): 23:1-23:24 (2021) - [j26]Chongyang Wang, Yuan Gao, Akhil Mathur, Amanda C. de C. Williams, Nicholas D. Lane, Nadia Bianchi-Berthouze:
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(2): 81:1-81:27 (2021) - [j25]Catherine Tong, Jinchen Ge, Nicholas D. Lane:
Zero-Shot Learning for IMU-Based Activity Recognition Using Video Embeddings. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(4): 180:1-180:23 (2021) - [j24]HyeokHyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao, Gregory D. Abowd, Nicholas D. Lane, Thomas Ploetz:
Can You See It?: Good, So We Can Sense It! GetMobile Mob. Comput. Commun. 25(2): 38-42 (2021) - [c117]Yan Gao, Titouan Parcollet, Nicholas D. Lane:
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition. ASRU 2021: 138-145 - [c116]Adrian Bulat, Jean Kossaifi, Sourav Bhattacharya, Yannis Panagakis, Timothy M. Hospedales, Georgios Tzimiropoulos, Nicholas D. Lane, Maja Pantic:
Defensive Tensorization. BMVC 2021: 131 - [c115]Kwing Hei Li, Pedro Porto Buarque de Gusmão, Daniel J. Beutel, Nicholas D. Lane:
Secure aggregation for federated learning in flower. DistributedML@CoNEXT 2021: 8-14 - [c114]Edgar Liberis, Lukasz Dudziak, Nicholas D. Lane:
μNAS: Constrained Neural Architecture Search for Microcontrollers. EuroMLSys@EuroSys 2021: 70-79 - [c113]Stylianos I. Venieris, Javier Fernández-Marqués, Nicholas D. Lane:
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation. FCCM 2021: 165-175 - [c112]Lichuan Xiang, Royson Lee, Mohamed S. Abdelfattah, Nicholas D. Lane, Hongkai Wen:
Temporal Kernel Consistency for Blind Video Super-Resolution. ICCVW 2021: 3470-3479 - [c111]Mohamed S. Abdelfattah, Abhinav Mehrotra, Lukasz Dudziak, Nicholas Donald Lane:
Zero-Cost Proxies for Lightweight NAS. ICLR 2021 - [c110]Abhinav Mehrotra, Alberto Gil C. P. Ramos, Sourav Bhattacharya, Lukasz Dudziak, Ravichander Vipperla, Thomas Chau, Mohamed S. Abdelfattah, Samin Ishtiaq, Nicholas Donald Lane:
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition. ICLR 2021 - [c109]Shyam Anil Tailor, Javier Fernández-Marqués, Nicholas Donald Lane:
Degree-Quant: Quantization-Aware Training for Graph Neural Networks. ICLR 2021 - [c108]Mário Almeida, Stefanos Laskaridis, Abhinav Mehrotra, Lukasz Dudziak, Ilias Leontiadis, Nicholas D. Lane:
Smart at what cost?: characterising mobile deep neural networks in the wild. Internet Measurement Conference 2021: 658-672 - [c107]Stefanos Laskaridis, Alexandros Kouris, Nicholas D. Lane:
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions. EMDL@MobiSys 2021: 1-6 - [c106]Qingqing Cao, Alexandru Eugen Irimiea, Mohamed S. Abdelfattah, Aruna Balasubramanian, Nicholas D. Lane:
Are Mobile DNN Accelerators Accelerating DNNs? EMDL@MobiSys 2021: 7-12 - [c105]Samuel Horváth, Stefanos Laskaridis, Mário Almeida, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane:
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. NeurIPS 2021: 12876-12889 - [c104]Rui Li, Ondrej Bohdal, Rajesh K. Mishra, Hyeji Kim, Da Li, Nicholas D. Lane, Timothy M. Hospedales:
A Channel Coding Benchmark for Meta-Learning. NeurIPS Datasets and Benchmarks 2021 - [c103]Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris, Nicholas D. Lane:
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation. HotMobile 2021: 15-21 - [i58]Emma Rocheteau, Catherine Tong, Petar Velickovic, Nicholas D. Lane, Pietro Liò:
Predicting Patient Outcomes with Graph Representation Learning. CoRR abs/2101.03940 (2021) - [i57]Mohamed S. Abdelfattah, Abhinav Mehrotra, Lukasz Dudziak, Nicholas D. Lane:
Zero-Cost Proxies for Lightweight NAS. CoRR abs/2101.08134 (2021) - [i56]Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris, Nicholas D. Lane:
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation. CoRR abs/2102.01393 (2021) - [i55]Xinchi Qiu, Titouan Parcollet, Javier Fernández-Marqués, Pedro Porto Buarque de Gusmão, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane:
A first look into the carbon footprint of federated learning. CoRR abs/2102.07627 (2021) - [i54]Samuel Horváth, Stefanos Laskaridis, Mário Almeida, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane:
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. CoRR abs/2102.13451 (2021) - [i53]Stylianos I. Venieris, Javier Fernández-Marqués, Nicholas D. Lane:
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation. CoRR abs/2103.05600 (2021) - [i52]Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas D. Lane:
Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions. CoRR abs/2104.01481 (2021) - [i51]