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ORCIDJavier Fernández-Marqués
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2020 – today
- 2024
[c17]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- [c16]Mohammad Naseri
, Javier Fernández-Marqués, Yan Gao, Heng Pan:
Privacy-Preserving Federated Learning using Flower Framework. KDD 2024: 6422-6423 - [i18]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) - [i17]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
- [j2]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) - [j1]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) - [i16]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) - [i15]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) - [i14]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) - [i13]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
- [c15]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 - [c14]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 - [c13]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 - [c12]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 - [c11]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 - [c10]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 - [i12]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) - [i11]Lukasz Dudziak, Stefanos Laskaridis, Javier Fernández-Marqués:
FedorAS: Federated Architecture Search under system heterogeneity. CoRR abs/2206.11239 (2022) - [i10]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) - [i9]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) - [i8]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) - [i7]Zicheng Liu, Da Li, Javier Fernández-Marqués, Stefanos Laskaridis, Yan Gao, Lukasz Dudziak, Stan Z. Li, Shell Xu Hu, Timothy M. Hospedales:
Federated Learning for Inference at Anytime and Anywhere. CoRR abs/2212.04084 (2022) - 2021
- [c9]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 - [c8]Shyam Anil Tailor, Javier Fernández-Marqués, Nicholas Donald Lane:
Degree-Quant: Quantization-Aware Training for Graph Neural Networks. ICLR 2021 - [i6]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) - [i5]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) - [i4]Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão, Javier Fernández-Marqués, Taner Topal, Xinchi Qiu, Titouan Parcollet, Yan Gao, Nicholas D. Lane:
On-device Federated Learning with Flower. CoRR abs/2104.03042 (2021) - [i3]Yan Gao, Titouan Parcollet, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Daniel J. Beutel, Nicholas D. Lane:
End-to-End Speech Recognition from Federated Acoustic Models. CoRR abs/2104.14297 (2021) - 2020
- [c7]Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina:
Searching for Winograd-aware Quantized Networks. MLSys 2020 - [i2]Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina:
Searching for Winograd-aware Quantized Networks. CoRR abs/2002.10711 (2020) - [i1]Shyam A. Tailor, Javier Fernández-Marqués, Nicholas D. Lane:
Degree-Quant: Quantization-Aware Training for Graph Neural Networks. CoRR abs/2008.05000 (2020)
2010 – 2019
- 2019
- [c6]Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal:
An Empirical study of Binary Neural Networks' Optimisation. ICLR (Poster) 2019 - 2018
- [c5]Vincent W. S. Tseng, Sourav Bhattacharya, Javier Fernández-Marqués, Milad Alizadeh, Catherine Tong, Nicholas D. Lane:
Deterministic Binary Filters for Convolutional Neural Networks. IJCAI 2018: 2739-2747 - [c4]Javier Fernández-Marqués, Vincent W. S. Tseng, Sourav Bhattacharya, Nicholas D. Lane:
On-the-fly deterministic binary filters for memory efficient keyword spotting applications on embedded devices. EMDL@MobiSys 2018: 13-18 - [c3]Javier Fernández-Marqués, Vincent W. S. Tseng, Sourav Bhattacharya, Nicholas D. Lane:
Deterministic binary filters for keyword spotting applications. MobiSys 2018: 529 - 2015
- [c2]María Anguiano, Carlos Castilla, Martin Maska, Cristina Ederra, Javier Fernández-Marqués, Rafael Peláez, Ana Rouzaut, Arrate Muñoz-Barrutia, Michal Kozubek, Carlos Ortiz-de-Solorzano:
Characterization of the role of collagen network structure and composition in cancer cell migration. EMBC 2015: 8139-8142 - [c1]Martin Maska, Cristina Ederra, Javier Fernández-Marqués, Arrate Muñoz-Barrutia, Michal Kozubek, Carlos Ortiz-de-Solorzano:
Quantification of the 3D collagen network geometry in confocal reflection microscopy. ICIP 2015: 1791-1794
Coauthor Index
aka: Pedro P. B. de Gusmao
aka: Nicholas Donald Lane
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