7 citations · 9 across the 5 of their papers we have counts for
7 papers
Category learning in deep neural networks: Information content and geometry of internal representations
Laurent Bonnasse-Gahot, Jean-Pierre Nadal
In humans and other animals, category learning enhances discrimination between stimuli close to the category boundary. This phenomenon, called categorical perception, was also empi…
Acoustic characterization of speech rhythm: going beyond metrics with recurrent neural networks
François Deloche, Laurent Bonnasse-Gahot, Judit Gervain
Languages have long been described according to their perceived rhythmic attributes. The associated typologies are of interest in psycholinguistics as they partly predict newborns'…
Information theoretic study of the neural geometry induced by category learning
Laurent Bonnasse-Gahot, Jean-Pierre Nadal
Categorization is an important topic both for biological and artificial neural networks. Here, we take an information theoretic approach to assess the efficiency of the representat…
Interpolation, extrapolation, and local generalization in common neural networks
Laurent Bonnasse-Gahot
There has been a long history of works showing that neural networks have hard time extrapolating beyond the training set. A recent study by Balestriero et al. (2021) challenges thi…
Categorical Perception: A Groundwork for Deep Learning
Laurent Bonnasse-Gahot, Jean-Pierre Nadal
A well-known perceptual consequence of categorization in humans and other animals, called categorical perception, is notably characterized by a within-category compression and a be…
KerCNNs: biologically inspired lateral connections for classification of corrupted images
Noemi Montobbio, Laurent Bonnasse-Gahot, Giovanna Citti +1
The state of the art in many computer vision tasks is represented by Convolutional Neural Networks (CNNs). Although their hierarchical organization and local feature extraction are…