6 citations · 11 across the 21 of their papers we have counts for
13 papers · 1 filter
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…
A Geometric Unification of Concept Learning with Concept Cones
Alexandre Rocchi, Thomas Fel, Gianni Franchi
Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse…
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 how we extract representations from scientific data, yet the statistical properties of these representations remain poorly control…
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…
Visual Anagrams Reveal Hidden Differences in Holistic Shape Processing Across Vision Models
Fenil R. Doshi, Thomas Fel, Talia Konkle +1
Humans are able to recognize objects based on both local texture cues and the configuration of object parts, yet contemporary vision models primarily harvest local texture cues, yi…
Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
Matyas Bohacek, Thomas Fel, Maneesh Agrawala +1
Despite their impressive performance, generative image models trained on large-scale datasets frequently fail to produce images with seemingly simple concepts -- e.g., human hands…
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…
Interpreting the linear structure of vision-language model embedding spaces
Isabel Papadimitriou, Huangyuan Su, Thomas Fel +2
Vision-language models encode images and text in a joint space, minimizing the distance between corresponding image and text pairs. How are language and images organized in this jo…
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…
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince +7
Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dicti…
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
Harrish Thasarathan, Julian Forsyth, Thomas Fel +2
We present Universal Sparse Autoencoders (USAEs), a framework for uncovering and aligning interpretable concepts spanning multiple pretrained deep neural networks. Unlike existing…