Search Results for author: Dorina Thanou

Found 15 papers, 6 papers with code

Generative Modelling of Structurally Constrained Graphs

1 code implementation25 Jun 2024 Manuel Madeira, Clement Vignac, Dorina Thanou, Pascal Frossard

We present ConStruct, a novel framework that allows for hard-constraining graph diffusion models to incorporate specific properties, such as planarity or acyclicity.

Graph Generation

Tertiary Lymphoid Structures Generation through Graph-based Diffusion

no code implementations10 Oct 2023 Manuel Madeira, Dorina Thanou, Pascal Frossard

In particular, we show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content, a well-established biomarker for evaluating the cancer progression in oncology research.

Data Augmentation

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.

Interpretable Stability Bounds for Spectral Graph Filters

no code implementations18 Feb 2021 Henry Kenlay, Dorina Thanou, Xiaowen Dong

In this paper, we study filter stability and provide a novel and interpretable upper bound on the change of filter output, where the bound is expressed in terms of the endpoint degrees of the deleted and newly added edges, as well as the spatial proximity of those edges.

Anomaly Detection Denoising

On the Stability of Graph Convolutional Neural Networks under Edge Rewiring

no code implementations ICLR Workshop GTRL 2021 Henry Kenlay, Dorina Thanou, Xiaowen Dong

Graph neural networks are experiencing a surge of popularity within the machine learning community due to their ability to adapt to non-Euclidean domains and instil inductive biases.

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

no code implementations31 Jul 2020 Xiaowen Dong, Dorina Thanou, Laura Toni, Michael Bronstein, Pascal Frossard

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.

BIG-bench Machine Learning Computational Efficiency

node2coords: Graph Representation Learning with Wasserstein Barycenters

no code implementations31 Jul 2020 Effrosyni Simou, Dorina Thanou, Pascal Frossard

In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns.

Decoder Graph Representation Learning +1

Mask Combination of Multi-layer Graphs for Global Structure Inference

1 code implementation22 Oct 2019 Eda Bayram, Dorina Thanou, Elif Vural, Pascal Frossard

Structure inference is an important task for network data processing and analysis in data science.

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

no code implementations25 Apr 2019 Sarah Itani, Dorina Thanou

Finally, we use these new markers to train a decision tree, an interpretable classification scheme, which results in a final diagnosis aid model.

General Classification

Learning graphs from data: A signal representation perspective

no code implementations3 Jun 2018 Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data.

Graph Learning

Graph learning under sparsity priors

1 code implementation18 Jul 2017 Hermina Petric Maretic, Dorina Thanou, Pascal Frossard

If this is not possible, the data structure has to be inferred from the mere signal observations.

Graph Learning

Learning heat diffusion graphs

no code implementations4 Nov 2016 Dorina Thanou, Xiaowen Dong, Daniel Kressner, Pascal Frossard

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph.

Graph Learning

Graph-based compression of dynamic 3D point cloud sequences

no code implementations19 Jun 2015 Dorina Thanou, Philip A. Chou, Pascal Frossard

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes.

Motion Estimation

Learning Laplacian Matrix in Smooth Graph Signal Representations

2 code implementations30 Jun 2014 Xiaowen Dong, Dorina Thanou, Pascal Frossard, Pierre Vandergheynst

We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals.

Graph Learning

Learning parametric dictionaries for graph signals

1 code implementation5 Jan 2014 Dorina Thanou, David I Shuman, Pascal Frossard

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.

Denoising Dictionary Learning

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