4 citations · 4 across the 2 of their papers we have counts for
9 papers
Can neurons speak? Semantic narration of vision at single-cell resolution
Arnau Marin-Llobet, Richard Hakim, Sara Matias +3
Identifying what individual neurons encode in higher-order visual cortex is an open problem. Responses resist intuitive parameterization, and the deep-network embeddings used in th…
Into the Rabbit Hull: From Task-Relevant Concepts in DINO to Minkowski Geometry
Thomas Fel, Binxu Wang, Michael A. Lepori +8
DINOv2 is routinely deployed to recognize objects, scenes, and actions; yet the nature of what it perceives remains unknown. As a working baseline, we adopt the Linear Representati…
Block-Recurrent Dynamics in Vision Transformers
Mozes Jacobs, Thomas Fel, Richard Hakim +3
As Vision Transformers (ViTs) become standard vision backbones, a mechanistic account of their computational phenomenology is essential. Despite architectural cues that hint at dyn…
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…
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…
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…