45 citations · 100 across the 29 of their papers we have counts for
12 papers · 1 filter
DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems
Vincent Gripon, Carlos Lassance, Ghouthi Boukli Hacene
Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years. Indeed, Deep Neural Networks are now the golden standard…
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Carlos Lassance, Louis Béthune, Myriam Bontonou +2
Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task. In this work, we propose exploiting Latent Geometry…
Representing Deep Neural Networks Latent Space Geometries with Graphs
Carlos Lassance, Vincent Gripon, Antonio Ortega
Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists…
Gradients of Connectivity as Graph Fourier Bases of Brain Activity
Giulia Lioi, Vincent Gripon, Abdelbasset Brahim +2
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neu…
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
Lyes Khacef, Vincent Gripon, Benoit Miramond
Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur…
ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget
Guillaume Coiffier, Ghouthi Boukli Hacene, Vincent Gripon
Typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through down…