no code implementations • 2 Feb 2023 • Fernando Gama, Nicolas Zilberstein, Martin Sevilla, Richard Baraniuk, Santiago Segarra

Thus, the crux of particle filters lies in designing sampling distributions that are both easy to sample from and lead to accurate estimators.

no code implementations • 16 Nov 2022 • Elvin Isufi, Fernando Gama, David I. Shuman, Santiago Segarra

For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks.

1 code implementation • 28 Oct 2022 • Fengjun Yang, Fernando Gama, Somayeh Sojoudi, Nikolai Matni

Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and information exchange structure, e. g., sparsity, delay, or spatial invariance.

no code implementations • 17 Oct 2022 • Damian Owerko, Fernando Gama, Alejandro Ribeiro

Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost.

no code implementations • 8 Jul 2022 • Alejandro Parada-Mayorga, Zhiyang Wang, Fernando Gama, Alejandro Ribeiro

We also conclude that in Agg-GNNs the selectivity of the mapping operators is tied to the properties of the filters only in the first layer of the CNN stage.

no code implementations • 22 Feb 2022 • T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk, Santiago Segarra

Leveraging this, we are able to seamlessly compare graphs of different sizes and coming from different models, yielding results on the convergence of spectral densities, transferability of filters across arbitrary graphs, and continuity of graph signal properties with respect to the distribution of local substructures.

1 code implementation • 14 Oct 2021 • Arindam Chowdhury, Fernando Gama, Santiago Segarra

Power allocation is one of the fundamental problems in wireless networks and a wide variety of algorithms address this problem from different perspectives.

1 code implementation • 6 Oct 2021 • Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra

Particle filtering is used to compute good nonlinear estimates of complex systems.

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.

no code implementations • 19 Jul 2021 • Zhan Gao, Fernando Gama, Alejandro Ribeiro

At training time, the joint wide and deep architecture learns nonlinear representations from data.

1 code implementation • 24 Jun 2021 • Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler

Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN / GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively.

no code implementations • 31 May 2021 • Fernando Gama, Brendon G. Anderson, Somayeh Sojoudi

We show that, by replacing nonlinear activation functions by NVGFs, frequency creation mechanisms can be designed or learned.

no code implementations • 15 Mar 2021 • Fernando Gama, Somayeh Sojoudi

Controlling network systems has become a problem of paramount importance.

no code implementations • 29 Dec 2020 • Fernando Gama, QingBiao Li, Ekaterina Tolstaya, Amanda Prorok, Alejandro Ribeiro

Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information.

no code implementations • 10 Nov 2020 • Fernando Gama, Somayeh Sojoudi

When considering a network system, this renders the optimal controller a centralized one.

no code implementations • 27 Oct 2020 • Luana Ruiz, Fernando Gama, Alejandro Ribeiro, Elvin Isufi

In this work, we approach GCNNs from a state-space perspective revealing that the graph convolutional module is a minimalistic linear state-space model, in which the state update matrix is the graph shift operator.

no code implementations • 17 Oct 2020 • Samuel Pfrommer, Fernando Gama, Alejandro Ribeiro

We define a notion of discriminability tied to the stability of the architecture, show that GNNs are at least as discriminative as linear graph filter banks, and characterize the signals that cannot be discriminated by either.

no code implementations • 12 Oct 2020 • Zhan Gao, Fernando Gama, Alejandro Ribeiro

Spherical convolutional neural networks (Spherical CNNs) learn nonlinear representations from 3D data by exploiting the data structure and have shown promising performance in shape analysis, object classification, and planning among others.

no code implementations • 4 Aug 2020 • Luana Ruiz, Fernando Gama, Alejandro Ribeiro

They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters.

no code implementations • 11 Jun 2020 • Zhan Gao, Fernando Gama, Alejandro Ribeiro

At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem.

no code implementations • 23 Mar 2020 • Fernando Gama, Ekaterina Tolstaya, Alejandro Ribeiro

Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities.

1 code implementation • 8 Mar 2020 • Fernando Gama, Elvin Isufi, Geert Leus, Alejandro Ribeiro

We also introduce GNN extensions using edge-varying and autoregressive moving average graph filters and discuss their properties.

no code implementations • 6 Feb 2020 • Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler

More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots.

1 code implementation • 3 Feb 2020 • Luana Ruiz, Fernando Gama, Alejandro Ribeiro

Graph processes exhibit a temporal structure determined by the sequence index and and a spatial structure determined by the graph support.

1 code implementation • 21 Jan 2020 • Elvin Isufi, Fernando Gama, Alejandro Ribeiro

This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs).

1 code implementation • 12 Dec 2019 • Qing-Biao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok

We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations.

no code implementations • 21 Oct 2019 • Fernando Gama, Joan Bruna, Alejandro Ribeiro

In this paper, we are set to study the effect that a change in the underlying graph topology that supports the signal has on the output of a GNN.

1 code implementation • NeurIPS 2019 • Fernando Gama, Joan Bruna, Alejandro Ribeiro

In this work, we extend scattering transforms to network data by using multiresolution graph wavelets, whose computation can be obtained by means of graph convolutions.

no code implementations • 11 May 2019 • Fernando Gama, Joan Bruna, Alejandro Ribeiro

Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of graph signals, exhibiting success in recommender systems, power outage prediction, and motion planning, among others.

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.

1 code implementation • 25 Mar 2019 • Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar, Alejandro Ribeiro

We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications.

Robotics

1 code implementation • 5 Mar 2019 • Luana Ruiz, Fernando Gama, Alejandro Ribeiro

Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather.

Ranked #11 on Node Classification on CiteSeer (0.5%)

no code implementations • 4 Mar 2019 • Elvin Isufi, Fernando Gama, Alejandro Ribeiro

This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters.

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 • ICLR 2019 • Fernando Gama, Alejandro Ribeiro, Joan Bruna

Stability is a key aspect of data analysis.

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.

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