Dorina Thanou

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EPFL

Interpretable Stability Bounds for Spectral Graph Filters

Graph-structured data arise in a variety of real-world context ranging f...
02/18/2021 ∙ by Henry Kenlay, et al. ∙ 7 ∙ share

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On the Stability of Graph Convolutional Neural Networks under Edge Rewiring

Graph neural networks are experiencing a surge of popularity within the ...
10/26/2020 ∙ by Henry Kenlay, et al. ∙ 12 ∙ share

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Graph signal processing for machine learning: A review and new perspectives

The effective representation, processing, analysis, and visualization of...
07/31/2020 ∙ by Xiaowen Dong, et al. ∙ 46 ∙ share

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node2coords: Graph Representation Learning with Wasserstein Barycenters

In order to perform network analysis tasks, representations that capture...
07/31/2020 ∙ by Effrosyni Simou, et al. ∙ 0 ∙ share

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Mask Combination of Multi-layer Graphs for Global Structure Inference

Structure inference is an important task for network data processing and...
10/22/2019 ∙ by Eda Bayram, et al. ∙ 10 ∙ share

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Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder

While the prevalence of Autism Spectrum Disorder (ASD) is increasing, re...
04/25/2019 ∙ by Sarah Itani, et al. ∙ 0 ∙ share

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Learning Graphs from Data: A Signal Representation Perspective

The construction of a meaningful graph topology plays a crucial role in ...
06/03/2018 ∙ by Xiaowen Dong, et al. ∙ 0 ∙ share

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Graph-based Transform Coding with Application to Image Compression

In this paper, we propose a new graph-based coding framework and illustr...
12/18/2017 ∙ by Giulia Fracastoro, et al. ∙ 0 ∙ share

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Graph learning under sparsity priors

Graph signals offer a very generic and natural representation for data t...
07/18/2017 ∙ by Hermina Petric Maretic, et al. ∙ 0 ∙ share

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Learning heat diffusion graphs

Effective information analysis generally boils down to properly identify...
11/04/2016 ∙ by Dorina Thanou, et al. ∙ 0 ∙ share

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Graph-based compression of dynamic 3D point cloud sequences

This paper addresses the problem of compression of 3D point cloud sequen...
06/19/2015 ∙ by Dorina Thanou, et al. ∙ 0 ∙ share

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Learning Laplacian Matrix in Smooth Graph Signal Representations

The construction of a meaningful graph plays a crucial role in the succe...
06/30/2014 ∙ by Xiaowen Dong, et al. ∙ 0 ∙ share

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Learning parametric dictionaries for graph signals

In sparse signal representation, the choice of a dictionary often involv...
01/05/2014 ∙ by Dorina Thanou, et al. ∙ 0 ∙ share

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Dorina Thanou

Featured Co-authors

Pascal Frossard

Pierre Vandergheynst

Michael Bronstein

Xiaowen Dong

Michael Rabbat

Daniel Kressner

Laura Toni

Elif Vural

Giulia Fracastoro

Philip A. Chou

Hermina Petric Maretic

Interpretable Stability Bounds for Spectral Graph Filters

On the Stability of Graph Convolutional Neural Networks under Edge Rewiring

Graph signal processing for machine learning: A review and new perspectives

node2coords: Graph Representation Learning with Wasserstein Barycenters

Mask Combination of Multi-layer Graphs for Global Structure Inference

Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder

Learning Graphs from Data: A Signal Representation Perspective

Graph-based Transform Coding with Application to Image Compression

Graph learning under sparsity priors

Learning heat diffusion graphs

Graph-based compression of dynamic 3D point cloud sequences

Learning Laplacian Matrix in Smooth Graph Signal Representations

Learning parametric dictionaries for graph signals