Gonzalo Mateos | DeepAI

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∙ 07/25/2023

Gradient-Based Spectral Embeddings of Random Dot Product Graphs

The Random Dot Product Graph (RDPG) is a generative model for relational...

0 Marcelo Fiori, et al. ∙

∙ 05/27/2023

Explainable Brain Age Prediction using coVariance Neural Networks

In computational neuroscience, there has been an increased interest in d...

0 Saurabh Sihag, et al. ∙

∙ 05/02/2023

Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features

Graph convolutional networks (GCN) leverage topology-driven graph convol...

0 Saurabh Sihag, et al. ∙

∙ 03/20/2023

Fairness-Aware Graph Filter Design

Graphs are mathematical tools that can be used to represent complex real...

0 O. Deniz Kose, et al. ∙

∙ 11/07/2022

pyGSL: A Graph Structure Learning Toolkit

We introduce pyGSL, a Python library that provides efficient implementat...

0 Max Wasserman, et al. ∙

∙ 10/28/2022

Predicting Brain Age using Transferable coVariance Neural Networks

The deviation between chronological age and biological age is a well-rec...

0 Saurabh Sihag, et al. ∙

∙ 05/31/2022

coVariance Neural Networks

Graph neural networks (GNN) are an effective framework that exploit inte...

0 Saurabh Sihag, et al. ∙

∙ 05/19/2022

Learning Graph Structure from Convolutional Mixtures

Machine learning frameworks such as graph neural networks typically rely...

0 Max Wasserman, et al. ∙

∙ 01/26/2022

Online Change Point Detection for Weighted and Directed Random Dot Product Graphs

Given a sequence of random (directed and weighted) graphs, we address th...

0 Bernardo Marenco, et al. ∙

∙ 10/19/2021

Accelerated Graph Learning from Smooth Signals

We consider network topology identification subject to a signal smoothne...

0 Seyed Saman Saboksayr, et al. ∙

∙ 03/05/2021

Online Graph Learning under Smoothness Priors

The growing success of graph signal processing (GSP) approaches relies h...

0 Seyed Saman Saboksayr, et al. ∙

∙ 07/16/2020

Graph topology inference benchmarks for machine learning

Graphs are nowadays ubiquitous in the fields of signal processing and ma...

0 Carlos Lassance, et al. ∙

∙ 03/12/2018

Blind Identification of Invertible Graph Filters with Multiple Sparse Inputs

This paper deals with problem of blind identification of a graph filter ...

0 Chang Ye, et al. ∙

∙ 10/31/2017

Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation

We study the problem of sampling a bandlimited graph signal in the prese...

0 Abolfazl Hashemi, et al. ∙

∙ 03/30/2015

Decentralized learning for wireless communications and networking

This chapter deals with decentralized learning algorithms for in-network...

0 Georgios B. Giannakis, et al. ∙

∙ 04/17/2014

Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

Extracting latent low-dimensional structure from high-dimensional data i...

0 Morteza Mardani, et al. ∙

∙ 01/31/2013

Rank regularization and Bayesian inference for tensor completion and extrapolation

A novel regularizer of the PARAFAC decomposition factors capturing the t...

0 Juan Andres Bazerque, et al. ∙

∙ 11/29/2012

Dynamic Network Cartography

Communication networks have evolved from specialized, research and tacti...

0 Gonzalo Mateos, et al. ∙

∙ 04/30/2012

Recovery of Low-Rank Plus Compressed Sparse Matrices with Application to Unveiling Traffic Anomalies

Given the superposition of a low-rank matrix plus the product of a known...

0 Morteza Mardani, et al. ∙

∙ 03/07/2012

In-network Sparsity-regularized Rank Minimization: Algorithms and Applications

Given a limited number of entries from the superposition of a low-rank m...

0 Morteza Mardani, et al. ∙

∙ 11/08/2011

Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization

Principal component analysis (PCA) is widely used for dimensionality red...

0 Gonzalo Mateos, et al. ∙

∙ 04/04/2011

Robust Nonparametric Regression via Sparsity Control with Application to Load Curve Data Cleansing

Nonparametric methods are widely applicable to statistical inference pro...

0 Gonzalo Mateos, et al. ∙

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