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Nicholas D. Lane

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FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients

Feb 15, 2024
Xinchi Qiu, Yan Gao, Lorenzo Sani, Heng Pan, Wanru Zhao, Pedro P. B. Gusmao, Mina Alibeigi, Alex Iacob, Nicholas D. Lane

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Federated Learning Priorities Under the European Union Artificial Intelligence Act

Feb 05, 2024
Herbert Woisetschläger, Alexander Erben, Bill Marino, Shiqiang Wang, Nicholas D. Lane, Ruben Mayer, Hans-Arno Jacobsen

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How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor

Nov 30, 2023
Hrushikesh Loya, Łukasz Dudziak, Abhinav Mehrotra, Royson Lee, Javier Fernandez-Marques, Nicholas D. Lane, Hongkai Wen

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Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads

Oct 17, 2023
Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris, Nicholas D. Lane

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FedL2P: Federated Learning to Personalize

Oct 03, 2023
Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy Hospedales, Ferenc Huszár, Nicholas D. Lane

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Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation

Jul 25, 2023
Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane

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TinyTrain: Deep Neural Network Training at the Extreme Edge

Jul 19, 2023
Young D. Kwon, Rui Li, Stylianos I. Venieris, Jagmohan Chauhan, Nicholas D. Lane, Cecilia Mascolo

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L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning

Jul 14, 2023
Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão, Mina Alibeigi, Jiajun Shen, Nicholas D. Lane

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FDAPT: Federated Domain-adaptive Pre-training for Language Models

Jul 12, 2023
Lekang Jiang, Filip Svoboda, Nicholas D. Lane

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FedVal: Different good or different bad in federated learning

Jun 06, 2023
Viktor Valadi, Xinchi Qiu, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Mina Alibeigi

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