3 papers
cs.LG2025
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…
cs.LG2025
Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies
Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel +3
Modern artificial intelligence has revolutionized our ability to extract rich and versatile data representations across scientific disciplines. Yet, the statistical properties of t…
cs.LG2025
Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
Sai Sumedh R. Hindupur, Ekdeep Singh Lubana, Thomas Fel +1
Sparse Autoencoders (SAEs) are widely used to interpret neural networks by identifying meaningful concepts from their representations. However, do SAEs truly uncover all concepts a…