Search Results for author: Fernando Gama

Found 38 papers, 12 papers with code

Unsupervised Learning of Sampling Distributions for Particle Filters

no code implementations2 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.

Design Synthesis

Graph Filters for Signal Processing and Machine Learning on Graphs

no code implementations16 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.

Time Series Time Series Analysis

Distributed Optimal Control of Graph Symmetric Systems via Graph Filters

1 code implementation28 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.

Unsupervised Optimal Power Flow Using Graph Neural Networks

no code implementations17 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.

Stability of Aggregation Graph Neural Networks

no code implementations8 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.

On Local Distributions in Graph Signal Processing

no code implementations22 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.

Stability Analysis of Unfolded WMMSE for Power Allocation

1 code implementation14 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.

A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters

1 code implementation2 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.

Denoising Node Classification

Wide and Deep Graph Neural Network with Distributed Online Learning

no code implementations19 Jul 2021 Zhan Gao, Fernando Gama, Alejandro Ribeiro

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

Movie Recommendation

Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

1 code implementation24 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.

Imitation Learning

Node-Variant Graph Filters in Graph Neural Networks

no code implementations31 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.

Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning

no code implementations29 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.

Imitation Learning

Graph Neural Networks for Distributed Linear-Quadratic Control

no code implementations10 Nov 2020 Fernando Gama, Somayeh Sojoudi

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

Self-Supervised Learning

Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

no code implementations27 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.

Authorship Attribution

Discriminability of Single-Layer Graph Neural Networks

no code implementations17 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.

Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)

no code implementations12 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.

Object Recognition

Graph Neural Networks: Architectures, Stability and Transferability

no code implementations4 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.

Recommendation Systems

Wide and Deep Graph Neural Networks with Distributed Online Learning

no code implementations11 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.

Graph Neural Networks for Decentralized Controllers

no code implementations23 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.

Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks

1 code implementation8 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.

Recommendation Systems

VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms

no code implementations6 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.

Gated Graph Recurrent Neural Networks

1 code implementation3 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.

EdgeNets:Edge Varying Graph Neural Networks

1 code implementation21 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).

Graph Attention

Graph Neural Networks for Decentralized Multi-Robot Path Planning

1 code implementation12 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.

Decision Making

Stability of Graph Neural Networks to Relative Perturbations

no code implementations21 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.

Movie Recommendation Recommendation Systems

Stability of Graph Scattering Transforms

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.

Transfer Learning

Stability Properties of Graph Neural Networks

no code implementations11 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.

Motion Planning Recommendation Systems

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

no code implementations29 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.

Authorship Attribution Recommendation Systems

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

1 code implementation25 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

Gated Graph Convolutional Recurrent Neural Networks

1 code implementation5 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.

Node Classification

Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs

no code implementations4 Mar 2019 Elvin Isufi, Fernando Gama, Alejandro Ribeiro

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

General Classification

Median activation functions for graph neural networks

no code implementations29 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.

MIMO Graph Filters for Convolutional Neural Networks

no code implementations6 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.

Convolutional Neural Networks Via Node-Varying Graph Filters

no code implementations27 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.