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Ntorina Thanou

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Related publications (39)

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Learning time varying graphs

Pascal Frossard, Ntorina Thanou, Andreas Loukas, Vasilis Kalofolias

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 ...

Ieee2017

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

Pascal Frossard, Ntorina Thanou, Laura Toni, Xiaowen Dong, Michael Bronstein

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Polynomial Filter Design for Quantized Consensus

Pascal Frossard, Effrosyni Kokiopoulou, Ntorina Thanou, Hyung Gon Park

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 ...

2010

Learning Parametric Dictionaries for Signals on Graphs

Pascal Frossard, Ntorina Thanou, David Shuman

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 ...

Institute of Electrical and Electronics Engineers2014

Graph learning under sparsity priors

Pascal Frossard, Ntorina Thanou, Hermina Petric Maretic

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. ...

Ieee2017

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

Omar Raita, Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Ortal Yona Senouf, Thabodhan Mahendiran

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 ...

BMJ PUBLISHING GROUP2023

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

Ntorina Thanou

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 ...

IEEE2020

Interpretable Stability Bounds for Spectral Graph Filters

Ntorina Thanou

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 ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Learning of robust spectral graph dictionaries for distributed processing

Pascal Frossard, Ntorina Thanou

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 ...

SPRINGEROPEN2018

Parametric dictionary learning for graph signals

Pascal Frossard, Ntorina Thanou, David Shuman

We propose a parametric dictionary learning algorithm to design structured dictionaries that sparsely represent graph signals. We incorporate the graph structure by forcing the learned dictionaries to be concatenations of subdictionaries that are polynomia ...

2013

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

Ntorina Thanou

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 ...

2023

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

Emmanuel Abbé, Ntorina Thanou, Mehdi Ali Gadiri

Europa Edition2023

Graph Transform Learning for Image Compression

Pascal Frossard, Ntorina Thanou, Giulia Fracastoro

2016

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

Emmanuel Abbé, Ntorina Thanou, Edward Carlo Giorgio Andò, Ortal Yona Senouf, Thabodhan Mahendiran

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 ...

Elsevier BV2024

Ntorina Thanou

Graph Chatbot

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

Graph learning under sparsity priors

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

Interpretable Stability Bounds for Spectral Graph Filters

Learning of robust spectral graph dictionaries for distributed processing

Parametric dictionary learning for graph signals

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

Anatomy-informed multimodal learning for myocardial infarction prediction

Distributed Signal Processing with Graph Spectral Dictionaries

Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT