7 citations · 20 across the 11 of their papers we have counts for
12 papers · 1 filter
Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs
Carlos Hurtado, Sarath Shekkizhar, Javier Ruiz-Hidalgo +1
Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dat…
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…
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Romain Cosentino, Anirvan Sengupta, Salman Avestimehr +4
Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotatio…
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…
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression
Sarath Shekkizhar, Antonio Ortega
An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this i…
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…
Neighborhood and Graph Constructions using Non-Negative Kernel Regression
Sarath Shekkizhar, Antonio Ortega
Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications. k-nearest neighbor~(kNN) and -neighborhood me…
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…
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Myriam Bontonou, Carlos Lassance, Ghouthi Boukli Hacene +3
We introduce a novel loss function for training deep learning architectures to perform classification. It consists in minimizing the smoothness of label signals on similarity graph…
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness
Carlos Eduardo Rosar Kos Lassance, Vincent Gripon, Antonio Ortega
For the past few years, Deep Neural Network (DNN) robustness has become a question of paramount importance. As a matter of fact, in sensitive settings misclassification can lead to…