Giannis Nikolentzos | DeepAI

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∙ 08/06/2023

Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series

Irregularly sampled multivariate time series are ubiquitous in several a...

0 Chrysoula Kosma, et al. ∙

∙ 07/11/2023

Supervised Attention Using Homophily in Graph Neural Networks

Graph neural networks have become the standard approach for dealing with...

0 Michail Chatzianastasis, et al. ∙

∙ 06/09/2023

Path Neural Networks: Expressive and Accurate Graph Neural Networks

Graph neural networks (GNNs) have recently become the standard approach ...

0 Gaspard Michel, et al. ∙

∙ 04/21/2023

What Do GNNs Actually Learn? Towards Understanding their Representations

In recent years, graph neural networks (GNNs) have achieved great succes...

0 Giannis Nikolentzos, et al. ∙

∙ 11/04/2022

Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations

In recent years, graph neural networks (GNNs) have emerged as a promisin...

0 Giannis Nikolentzos, et al. ∙

∙ 07/27/2022

Time Series Forecasting Models Copy the Past: How to Mitigate

Time series forecasting is at the core of important application domains ...

0 Chrysoula Kosma, et al. ∙

∙ 05/27/2022

Image Keypoint Matching using Graph Neural Networks

Image matching is a key component of many tasks in computer vision and i...

0 Nancy Xu, et al. ∙

∙ 12/01/2021

NLP Research and Resources at DaSciM, Ecole Polytechnique

DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, est...

0 Hadi Abdine, et al. ∙

∙ 02/17/2021

Ego-based Entropy Measures for Structural Representations on Graphs

Machine learning on graph-structured data has attracted high research in...

0 George Dasoulas, et al. ∙

∙ 09/10/2020

United We Stand: Transfer Graph Neural Networks for Pandemic Forecasting

The recent outbreak of COVID-19 has affected millions of individuals aro...

0 George Panagopoulos, et al. ∙

∙ 03/02/2020

EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

Neural networks for structured data like graphs have been studied extens...

0 Changmin Wu, et al. ∙

∙ 03/01/2020

Ego-based Entropy Measures for Structural Representations

In complex networks, nodes that share similar structural characteristics...

0 George Dasoulas, et al. ∙

∙ 08/17/2019

Revisiting the Graph Isomorphism Problem with Semidefinite Programming

We present a new algorithm for the graph isomorphism problem which solve...

0 Giannis Nikolentzos, et al. ∙

∙ 08/17/2019

Message Passing Attention Networks for Document Understanding

Most graph neural networks can be described in terms of message passing,...

0 Giannis Nikolentzos, et al. ∙

∙ 07/13/2019

k-hop Graph Neural Networks

Graph neural networks (GNNs) have emerged recently as a powerful archite...

0 Giannis Nikolentzos, et al. ∙

∙ 04/27/2019

Graph Kernels: A Survey

Graph kernels have attracted a lot of attention during the last decade, ...

0 Giannis Nikolentzos, et al. ∙

∙ 04/03/2019

Rep the Set: Neural Networks for Learning Set Representations

In several domains, data objects can be decomposed into sets of simpler ...

0 Konstantinos Skianis, et al. ∙

∙ 08/07/2018

Message Passing Graph Kernels

Graph kernels have recently emerged as a promising approach for tackling...

0 Giannis Nikolentzos, et al. ∙

∙ 06/06/2018

GraKeL: A Graph Kernel Library in Python

The problem of accurately measuring the similarity between graphs is at ...

0 Giannis Siglidis, et al. ∙

∙ 07/29/2017

Classifying Graphs as Images with Convolutional Neural Networks

The task of graph classification is currently dominated by graph kernels...

0 Antoine Jean-Pierre Tixier, et al. ∙

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