3 papers
cs.LG2026
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…
cs.SD2026
Direct Raw Audio Signal Processing via Reservoir Computing: An Investigation into 'Feature-Free' Architectures
Rinku Sebastian, Simon O Keefe, Martin A Trefzer
This paper evaluates Reservoir Computing (RC) as an autonomous, 'feature-free' framework for audio processing, designed to eliminate traditional, handcrafted feature extraction sta…
cs.ET2025
Reservoir Computing Benchmarks: a tutorial review and critique
Chester Wringe, Martin Trefzer, Susan Stepney
Reservoir Computing is an Unconventional Computation model to perform computation on various different substrates, such as recurrent neural networks or physical materials. The meth…