75 citations · 246 across the 14 of their papers we have counts for
5 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…
Bilinear gating of motor primitives: a principle linking dendritic computation to rapid goal-directed adaptation
Cristiano Capone, Luca Falorsi, Andrea Ciardiello +1
Movement requires the motor cortex to specify both \emph{what} action to produce and \emph{which goal} it serves, yet how individual neurons separate these factors is not understoo…
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…