218 citations · 315 across the 11 of their papers we have counts for
6 papers · 1 filter
Long-range and hierarchical language predictions in brains and algorithms
Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King
Deep learning has recently made remarkable progress in natural language processing. Yet, the resulting algorithms remain far from competing with the language abilities of the human…
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects
Charlotte Caucheteux, Alexandre Gramfort, Jean-Rémi King
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e.g. regular speech versus scram…
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Omar Chehab, Alexandre Defossez, Jean-Christophe Loiseau +2
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they ca…
Disentangling Syntax and Semantics in the Brain with Deep Networks
Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King
The activations of language transformers like GPT-2 have been shown to linearly map onto brain activity during speech comprehension. However, the nature of these activations remain…
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Juliette Millet, Jean-Remi King
Our ability to comprehend speech remains, to date, unrivaled by deep learning models. This feat could result from the brain's ability to fine-tune generic sound representations for…
Can RNNs learn Recursive Nested Subject-Verb Agreements?
Yair Lakretz, Théo Desbordes, Jean-Rémi King +3
One of the fundamental principles of contemporary linguistics states that language processing requires the ability to extract recursively nested tree structures. However, it remain…