3 citations · 3 across the 9 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…
When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability
ML Nissen Gonzalez, Melwina Albuquerque, Laurence Wroe +3
Mechanistic interpretability aims to break models into meaningful parts; verifying that two such parts implement the same computation is a prerequisite. Existing similarity measure…
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…