3 citations · 4 across the 8 of their papers we have counts for
3 papers · 1 filter
From Mechanistic to Compositional Interpretability
Ward Gauderis, Thomas Dooms, Steven T. Homer +2
Mechanistic interpretability aims to explain neural model behaviour by reverse-engineering learned computational structure into human-understandable components. Without a formal fr…
Bilinear autoencoders find interpretable manifolds
Thomas Dooms, Ward Gauderis, Geraint Wiggins +1
Sparse autoencoders have become a standard tool for uncovering interpretable latent representations in neural networks. Yet salient concepts often span manifolds that current linea…
BioOSS: A Bio-Inspired Oscillatory State System with Spatio-Temporal Dynamics
Zhongju Yuan, Geraint Wiggins, Dick Botteldooren
Today's deep learning architectures are primarily based on perceptron models, which do not capture the oscillatory dynamics characteristic of biological neurons. Although oscillato…