From the 1 of 5 linked papers with an AI index.
5 papers
Reservoir Computing with Heterogeneous Magnetic Metamaterials
R. Yagan, C. Swindells, I. T. Vidamour +6
Physical reservoir computing utilizes the intrinsic nonlinear and history-dependent dynamics of physical systems to perform machine-learning tasks with minimal training overhead. H…
Reproducible Reservoir Computing with Thermally Driven Superparamagnets: Controlling Temperature Sensitivity
Zhengfei Chen, Alex Welbourne, Matthew O. A. Ellis +3
The paper simulates how ambient temperature changes affect the dynamics of superparamagnetic nanodot reservoirs and shows that using heterogeneous nanodot sizes can keep performanc…
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…
Learning Nonlinear Heterogeneity in Physical Kolmogorov-Arnold Networks
Fabiana Taglietti, Andrea Pulici, Maxwell Roxburgh +10
Physical neural networks typically train linear synaptic weights while treating device nonlinearities as fixed. We show the opposite - by training the synaptic nonlinearity itself,…