activity
20242026
most citedWhat Makes Two Language Models Think Alike?

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Decoding Naturalistic Speech from fMRI with Temporal Modeling and Multimodal Alignment

Corentin Caris, Louis Jalouzot, Nona Rajabi +9

Reconstructing what a person perceives from their brain activity is a stringent test of whether neural recordings carry meaningful information. Naturalistic speech offers a rich te…

cs.LG2026

Cephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain

Antoine Collas, Louis Jalouzot, Géraud Ilinca +11

Cephalonauts One is a whole-brain 3 Tesla (3T) functional magnetic resonance imaging (fMRI) dataset recorded while subjects listened to audio podcasts. Three healthy subjects under…

q-bio.NC2025

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants

Louis Jalouzot, Alexis Thual, Yair Lakretz +2

We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8…

cs.CL2024★ 1 cited

What Makes Two Language Models Think Alike?

Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1

Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar represe…

cs.CL2024

Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations

Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1

Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding…