1 code implementation • 25 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.

no code implementations • 10 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.

1 code implementation • 13 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.

no code implementations • 18 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.

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.

no code implementations • 31 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.

no code implementations • 31 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.

1 code implementation • 22 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.

no code implementations • 25 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.

no code implementations • 3 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.

1 code implementation • 18 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.

no code implementations • 4 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.

no code implementations • 19 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.

2 code implementations • 30 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.

1 code implementation • 5 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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.