Search Results for author: Jhony H. Giraldo

Found 15 papers, 6 papers with code

Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks

no code implementations29 May 2024 Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes in a graph.

Continuous Product Graph Neural Networks

no code implementations29 May 2024 Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science.

Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction

1 code implementation28 Mar 2024 Jhon A. Castro-Correa, Jhony H. Giraldo, Mohsen Badiey, Fragkiskos D. Malliaros

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting.

Graph Neural Network Imputation +2

Source-Guided Similarity Preservation for Online Person Re-Identification

1 code implementation23 Feb 2024 Hamza Rami, Jhony H. Giraldo, Nicolas Winckler, Stéphane Lathuilière

Our framework is based on the extraction of a support set composed of source images that maximizes the similarity with the target data.

Online unsupervised domain adaptation Person Re-Identification

Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals

no code implementations29 Oct 2023 Mohamad H. Alizade, Aref Einizade, Jhony H. Giraldo

Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations.

Clustering Graph Learning

Time-varying Signals Recovery via Graph Neural Networks

no code implementations22 Feb 2023 Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros

The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series.

Decoder Graph Learning +3

Higher-order Sparse Convolutions in Graph Neural Networks

no code implementations21 Feb 2023 Jhony H. Giraldo, Sajid Javed, Arif Mahmood, Fragkiskos D. Malliaros, Thierry Bouwmans

Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.

On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks

1 code implementation5 Dec 2022 Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans, Fragkiskos D. Malliaros

Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains.

Representation Learning

Reconstruction of Time-varying Graph Signals via Sobolev Smoothness

1 code implementation13 Jul 2022 Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, Thierry Bouwmans

In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples.

On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases

1 code implementation1 Jul 2020 Jhony H. Giraldo, Thierry Bouwmans

To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing.

GraphBGS: Background Subtraction via Recovery of Graph Signals

no code implementations17 Jan 2020 Jhony H. Giraldo, Thierry Bouwmans

Several deep learning methods for background subtraction have been proposed in the literature with competitive performances.

Change Detection graph construction +3

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