16 papers
A Unifying Framework for Concept-Based Representational Similarity
Grégoire Dhimoïla, Victor Boutin, Agustin Martin Picard +2
Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions. However, concept alignment…
Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot
Jorge Chang Ortega, Bastien Le Lan, Thomas Serre +1
A central question in computational vision is whether human-like visual representations are better explained by discriminative or generative learning. Existing comparisons, however…
Capability Interpretability: Human Interpretability of Vision Foundation Models
Julien Colin, Lore Goetschalckx, Nuria Oliver +1
How interpretable are the features of leading vision models? The question is increasingly pressing as these models move from research benchmarks into high-stakes deployments, yet e…
Choosing the right basis for interpretability: Psychophysical comparison between neuron-based and dictionary-based representations
Julien Colin, Lore Goetschalckx, Thomas Fel +3
Interpretability research often adopts a neuron-centric lens, treating individual neurons as the fundamental units of explanation. However, neuron-level explanations can be undermi…
Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models
Louis Béthune, David Vigouroux, Yilun Du +3
What is the shortest path between two data points lying in a high-dimensional space? While the answer is trivial in Euclidean geometry, it becomes significantly more complex when t…
Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics
Sabine Muzellec, Andrea Alamia, Thomas Serre +1
Neural synchrony is hypothesized to play a crucial role in how the brain organizes visual scenes into structured representations, enabling the robust encoding of multiple objects w…