75 citations · 246 across the 14 of their papers we have counts for
7 papers · 1 filter
Low-power analogue neural networks with trainable nonlinear connections for continuous control
Ian T. Vidamour, Fernando Aguirre, Thomas J. Hayward +13
Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as sc…
Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning
Chao Han, Stefanos Ioannou, Luca Manneschi +4
We investigate neural ordinary and stochastic differential equations (neural ODEs and SDEs) to model stochastic dynamics in fully and partially observed environments within a model…
Unified Policy Value Decomposition for Rapid Adaptation
Cristiano Capone, Luca Falorsi, Andrea Ciardiello +1
Rapid adaptation in complex control systems remains a central challenge in reinforcement learning. We introduce a framework in which policy and value functions share a low-dimensio…
Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition
Nia Touko, Matthew O A Ellis, Cristiano Capone +3
Reliable long-term decoding of gestures from surface electromyography (EMG) is hindered by signal drift caused by electrode displacement, muscle fatigue, and/or posture changes. Al…
Noise-Aware Training of Neuromorphic Dynamic Device Networks
Luca Manneschi, Ian T. Vidamour, Kilian D. Stenning +13
Physical computing has the potential to enable widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and inter…
Exploiting Multiple Timescales in Hierarchical Echo State Networks
Luca Manneschi, Matthew O. A. Ellis, Guido Gigante +3
Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights whilst the internal reservoir is formed of fixed randomly…
Learning sparsity in reservoir computing through a novel bio-inspired algorithm
Luca Manneschi, Andrew C. Lin, Eleni Vasilaki
The mushroom body is the key network for the representation of learned olfactory stimuli in Drosophila and insects. The sparse activity of Kenyon cells, the principal neurons in th…