218 citations · 315 across the 11 of their papers we have counts for
11 papers
Language acquisition: do children and language models follow similar learning stages?
Linnea Evanson, Yair Lakretz, Jean-Rémi King
During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually…
Decoding speech perception from non-invasive brain recordings
Alexandre Défossez, Charlotte Caucheteux, Jérémy Rapin +2
Decoding speech from brain activity is a long-awaited goal in both healthcare and neuroscience. Invasive devices have recently led to major milestones in that regard: deep learning…
MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
Laura Gwilliams, Graham Flick, Alec Marantz +3
The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each particip…
Toward a realistic model of speech processing in the brain with self-supervised learning
Juliette Millet, Charlotte Caucheteux, Pierre Orhan +5
Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely…
Don't stop the training: continuously-updating self-supervised algorithms best account for auditory responses in the cortex
Pierre Orhan, Yves Boubenec, Jean-Rémi King
Over the last decade, numerous studies have shown that deep neural networks exhibit sensory representations similar to those of the mammalian brain, in that their activations linea…
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…