5 papers
Interference Beyond Geometry in Concept Extraction
Valérie Costa, Bahareh Tolooshams
Interference is commonly treated as geometric overlap between learned features. We introduce effective interference, which combines feature geometry and code statistics to capture…
Priors in Time: Missing Inductive Biases for Language Model Interpretability
Ekdeep Singh Lubana, Can Rager, Sai Sumedh R. Hindupur +13
Recovering meaningful concepts from language model activations is a central aim of interpretability. While existing feature extraction methods aim to identify concepts that are ind…
Evaluating Sparse Autoencoders: From Shallow Design to Matching Pursuit
Valérie Costa, Thomas Fel, Ekdeep Singh Lubana +2
Sparse autoencoders (SAEs) have recently become central tools for interpretability, leveraging dictionary learning principles to extract sparse, interpretable features from neural…
From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
Valérie Costa, Thomas Fel, Ekdeep Singh Lubana +2
Motivated by the hypothesis that neural network representations encode abstract, interpretable features as linearly accessible, approximately orthogonal directions, sparse autoenco…
A Decoupled Approach for Composite Sparse-plus-Smooth Penalized Optimization
Adrian Jarret, Valérie Costa, Julien Fageot
We consider a linear inverse problem whose solution is expressed as a sum of two components: one smooth and the other sparse. This problem is addressed by minimizing an objective f…