4 citations · 8 across the 5 of their papers we have counts for
8 papers
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Romain Cosentino, Sarath Shekkizhar, Mahdi Soltanolkotabi +2
Self-supervised learning (SSL) has emerged as a desirable paradigm in computer vision due to the inability of supervised models to learn representations that can generalize in doma…
Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs
David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo +1
Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret. However, convolutional layers consist of multi…
Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation
David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo +1
State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretabilit…
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…
Deep geometric knowledge distillation with graphs
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene +3
In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resour…
Structural Robustness for Deep Learning Architectures
Carlos Lassance, Vincent Gripon, Jian Tang +1
Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including…