Portrait de Vitoria Barin Pacela

Vitoria Barin Pacela

Doctorat - UdeM
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Causalité
Généralisation hors distribution (OOD)
Modèles probabilistes
Robustesse
Site web
Google Scholar
GitHub

Publications

Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
Vitoria Barin Pacela
Shruti Joshi
Isabela Camacho
Simon Lacoste-Julien
David Klindt
The linear representation hypothesis states that neural network activations encode high-level concepts as linear mixtures. However, under su… (voir plus)perposition, this encoding is a projection from a higher-dimensional concept space into a lower-dimensional activation space, and a linear decision boundary in the concept space need not remain linear after projection. In this setting, classical sparse coding methods with per-sample iterative inference leverage compressed sensing guarantees to recover latent factors. Sparse autoencoders (SAEs), on the other hand, amortise sparse inference into a fixed encoder, introducing a systematic gap. We show this amortisation gap persists across training set sizes, latent dimensions, and sparsity levels, causing SAEs to fail under out-of-distribution (OOD) compositional shifts. Through controlled experiments that decompose the failure, we identify dictionary learning -- not the inference procedure -- as the binding constraint: SAE-learned dictionaries point in substantially wrong directions, and replacing the encoder with per-sample FISTA on the same dictionary does not close the gap. An oracle baseline proves the problem is solvable with a good dictionary at all scales tested. Our results reframe the SAE failure as a dictionary learning challenge, not an amortisation problem, and point to scalable dictionary learning as the key open problem for sparse inference under superposition.
2026-03-29
arXiv (prépublication)
doi.org
arxiv.org