207 citations · 516 across the 63 of their papers we have counts for
7 papers · 1 filter
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation
Vincent Gripon, Ghouthi B. Hacene, Matthias Löwe +1
Transfer learning using deep neural networks as feature extractors has become increasingly popular over the past few years. It allows to obtain state-of-the-art accuracy on dataset…
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte +2
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such a…
Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise
Eliott Coyac, Vincent Gripon, Charlotte Langlais +1
The brain is a noisy system subject to energy constraints. These facts are rarely taken into account when modelling artificial neural networks. In this paper, we are interested in…
SimiNet: a Novel Method for Quantifying Brain Network Similarity
Ahmad Mheich, Mahmoud Hassan, Mohamad Khalil +3
Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the propert…
A neighborhood-preserving translation operator on graphs
Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte +2
In this paper, we introduce translation operators on graphs. Contrary to spectrally-defined translations in the framework of graph signal processing, our operators mimic neighborho…
Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin
We propose a simple and generic layer formulation that extends the properties of convolutional layers to any domain that can be described by a graph. Namely, we use the support of…
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup +1
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies be…