Publications by Ntorina Thanou

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We consider the problem of inferring the hidden structure of high-dimensional dynamic systems from the perspective of graph learning. In particular, we aim at capturing the dynamic relationships between nodes by a sequence of graphs. Our approach is motiva ...

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...

We consider the problem of distributed average consensus where sensors exchange quantized data with their neighbors. We deploy a polynomial filtering approach in the network nodes in order to accelerate the convergence of the consensus problem. The quantiz ...

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties – the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on wei ...

BackgroundAngiographic parameters can facilitate the risk stratification of coronary lesions but remain insufficient in the prediction of future myocardial infarction (MI).AIMSWe compared the ability of humans, angiographic parameters and deep learning (DL ...

Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes. In this case, data lying on the nodes of the graph are co ...

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks. As a ubiquitous tool to process graph-structured data, spectral graph filters have been used to solve common tasks such ...

We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We ...

In this paper, we study the problem of demixing an observed signal, which is the summation of a set of signals that live on a multi-layer graph, by proposing several methods to decompose the observed signal into structured components. For this purpose, we ...

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional c ...

Ntorina Thanou

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Learning time varying graphs

Graph Signal Processing for Machine Learning: A Review and New Perspectives

Polynomial Filter Design for Quantized Consensus

Learning Parametric Dictionaries for Signals on Graphs

Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study

A Graph Signal Processing Framework for the Classification of Temporal Brain Data

Graph learning under sparsity priors

Interpretable Stability Bounds for Spectral Graph Filters

Learning of robust spectral graph dictionaries for distributed processing

Graph Signal Separation Based on Smoothness or Sparsity in the Frequency Domain

Towards AI-assisted cardiology: a reflection on the performance and limitations of using large language models in clinical decision-making

Graph Transform Learning for Image Compression

AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

Graph-based motion estimation and compression for dynamic 3D point cloud compression

Learning sparse models of diffusive graph signals

Distributed Signal Processing with Graph Spectral Dictionaries