Luca Manneschi
Luca Manneschi
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A perspective on physical reservoir computing with nanomagnetic devices
DA Allwood, MOA Ellis, D Griffin, TJ Hayward, L Manneschi, MF Musameh, ...
Applied Physics Letters 122 (4), 2023
Exploiting multiple timescales in hierarchical echo state networks
L Manneschi, MOA Ellis, G Gigante, AC Lin, P Del Giudice, E Vasilaki
Frontiers in Applied Mathematics and Statistics 6, 616658, 2021
Reconfigurable reservoir computing in a magnetic metamaterial
IT Vidamour, C Swindells, G Venkat, L Manneschi, PW Fry, A Welbourne, ...
Communications Physics 6 (1), 230, 2023
SpaRCe: improved learning of reservoir computing systems through sparse representations
L Manneschi, AC Lin, E Vasilaki
IEEE Transactions on Neural Networks and Learning Systems 34 (2), 824-838, 2021
Neuromorphic few-shot learning: generalization in multilayer physical neural networks
KD Stenning, JC Gartside, L Manneschi, CTS Cheung, T Chen, ...
arXiv preprint arXiv:2211.06373, 2022
An alternative to backpropagation through time
L Manneschi, E Vasilaki
Nature Machine Intelligence 2 (3), 155-156, 2020
EchoVPR: Echo state networks for visual place recognition
A Özdemir, M Scerri, AB Barron, A Philippides, M Mangan, E Vasilaki, ...
IEEE Robotics and Automation Letters 7 (2), 4520-4527, 2022
Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy
L Manneschi, G Gigante, E Vasilaki, P Del Giudice
PLoS Computational Biology 18 (8), e1009393, 2022
Optimising network interactions through device agnostic models
L Manneschi, IT Vidamour, KD Stenning, JC Gartside, C Swindells, ...
arXiv preprint arXiv:2401.07387, 2024
Deep Physical Reservoir Computing with Programmable Nanomagnetic Hierarchies
K Stenning, J Gartside, L Manneschi, C Cheung, T Chen, J Love, ...
APS March Meeting Abstracts 2023, Q57. 014, 2023
Efficient Representations over Multiple Timescales.
L Manneschi
University of Sheffield, 2022
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