no code implementations • 3 Apr 2024 • Andrei Buciulea, Jiaxi Ying, Antonio G. Marques, Daniel P. Palomar
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals.
no code implementations • 22 Mar 2024 • Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
We consider fair network topology inference from nodal observations.
1 code implementation • 9 Feb 2024 • Sergio Martínez-Agüero, Antonio G. Marques, Inmaculada Mora-Jiménez, Joaquín Alvárez-Rodríguez, Cristina Soguero-Ruiz
Electronic health records (EHR) is an inherently multimodal register of the patient's health status characterized by static data and multivariate time series (MTS).
no code implementations • 16 Dec 2023 • Andrei Buciulea, Elvin Isufi, Geert Leus, Antonio G. Marques
Graphs are widely used to represent complex information and signal domains with irregular support.
1 code implementation • 11 Dec 2023 • Victor M. Tenorio, Samuel Rey, Antonio G. Marques
Graph Neural Networks (GNNs) have emerged as a notorious alternative to address learning problems dealing with non-Euclidean datasets.
no code implementations • 16 Sep 2023 • Victor M. Tenorio, Samuel Rey, Antonio G. Marques
Blind deconvolution over graphs involves using (observed) output graph signals to obtain both the inputs (sources) as well as the filter that drives (models) the graph diffusion process.
no code implementations • 16 Sep 2023 • Victor M. Tenorio, Madeline Navarro, Santiago Segarra, Antonio G. Marques
We present a framework to recover completely missing node features for a set of graphs, where we only know the signals of a subset of graphs.
no code implementations • 30 Jun 2023 • Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations.
no code implementations • 21 Mar 2023 • Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega, David I Shuman
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.
no code implementations • 13 Mar 2023 • Andrei Buciulea, Antonio G. Marques
Graphs have become pervasive tools to represent information and datasets with irregular support.
1 code implementation • 4 Dec 2022 • Samuel Rey, Madeline Navarro, Andrei Buciulea, Santiago Segarra, Antonio G. Marques
Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.
1 code implementation • 16 Oct 2022 • Samuel Rey, Victor M. Tenorio, Antonio G. Marques
Different from existing works, we formulate a non-convex optimization problem that operates in the vertex domain and jointly performs GF identification and graph denoising.
no code implementations • 11 Jul 2022 • Samuel Rey, T. Mitchell Roddenberry, Santiago Segarra, Antonio G. Marques
Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the network topology inference optimization problem.
1 code implementation • 21 Jan 2022 • Sergio Rozada, Santiago Paternain, Antonio G. Marques
Value-function (VF) approximation is a central problem in Reinforcement Learning (RL).
1 code implementation • 5 Oct 2021 • Samuel Rey, Andrei Buciulea, Madeline Navarro, Santiago Segarra, Antonio G. Marques
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference.
1 code implementation • 2 Oct 2021 • Victor M. Tenorio, Samuel Rey, Fernando Gama, Santiago Segarra, Antonio G. Marques
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular domains.
1 code implementation • 24 Sep 2021 • Samuel Rey, Santiago Segarra, Reinhard Heckel, Antonio G. Marques
This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios.
no code implementations • 31 May 2021 • David Ramírez, Antonio G. Marques, Santiago Segarra
When either the input or the filter coefficients are known, this is tantamount to assuming that the signals of interest live on a subspace defined by the supporting graph.
1 code implementation • 18 Apr 2021 • Sergio Rozada, Victor Tenorio, Antonio G. Marques
Value functions are central to Dynamic Programming and Reinforcement Learning but their exact estimation suffers from the curse of dimensionality, challenging the development of practical value-function (VF) estimation algorithms.
1 code implementation • 10 Mar 2021 • Samuel Rey, Antonio G. Marques
When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known.
no code implementations • 24 Dec 2020 • Fernando J. Iglesias Garcia, Santiago Segarra, Antonio G. Marques
Using graphs to model irregular information domains is an effective approach to deal with some of the intricacies of contemporary (network) data.
no code implementations • 16 Oct 2020 • Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra
Inferring graph structure from observations on the nodes is an important and popular network science task.
no code implementations • 2 Aug 2020 • Antonio G. Marques, Santiago Segarra, Gonzalo Mateos
This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs).
no code implementations • 15 Mar 2020 • Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis
The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and communications.
1 code implementation • 2 Aug 2019 • Samuel Rey, Antonio G. Marques, Santiago Segarra
While deep convolutional architectures have achieved remarkable results in a gamut of supervised applications dealing with images and speech, recent works show that deep untrained non-convolutional architectures can also outperform state-of-the-art methods in several tasks such as image compression and denoising.
no code implementations • 29 Mar 2019 • Luana Ruiz, Fernando Gama, Antonio G. Marques, Alejandro Ribeiro
Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made of stacked layers that compose graph convolutional filters with nonlinear activation functions.
no code implementations • 17 Dec 2018 • Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Marques, Georgios B. Giannakis
Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem.
1 code implementation • 5 Nov 2018 • Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis
The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering.
no code implementations • 29 Oct 2018 • Luana Ruiz, Fernando Gama, Antonio G. Marques, Alejandro Ribeiro
Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs.
no code implementations • 1 May 2018 • Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro
Multinode aggregation GNNs are consistently the best performing GNN architecture.
no code implementations • 6 Mar 2018 • Fernando Gama, Antonio G. Marques, Alejandro Ribeiro, Geert Leus
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks.
no code implementations • 27 Oct 2017 • Fernando Gama, Geert Leus, Antonio G. Marques, Alejandro Ribeiro
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks.