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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...
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07/11/2023
Supervised Attention Using Homophily in Graph Neural Networks
Graph neural networks have become the standard approach for dealing with...
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06/09/2023
Path Neural Networks: Expressive and Accurate Graph Neural Networks
Graph neural networks (GNNs) have recently become the standard approach ...
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04/21/2023
What Do GNNs Actually Learn? Towards Understanding their Representations
In recent years, graph neural networks (GNNs) have achieved great succes...
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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...
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07/27/2022
Time Series Forecasting Models Copy the Past: How to Mitigate
Time series forecasting is at the core of important application domains ...
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05/27/2022
Image Keypoint Matching using Graph Neural Networks
Image matching is a key component of many tasks in computer vision and i...
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12/01/2021
NLP Research and Resources at DaSciM, Ecole Polytechnique
DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, est...
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02/17/2021
Ego-based Entropy Measures for Structural Representations on Graphs
Machine learning on graph-structured data has attracted high research in...
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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...
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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...
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03/01/2020
Ego-based Entropy Measures for Structural Representations
In complex networks, nodes that share similar structural characteristics...
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08/17/2019
Revisiting the Graph Isomorphism Problem with Semidefinite Programming
We present a new algorithm for the graph isomorphism problem which solve...
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08/17/2019
Message Passing Attention Networks for Document Understanding
Most graph neural networks can be described in terms of message passing,...
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07/13/2019
k-hop Graph Neural Networks
Graph neural networks (GNNs) have emerged recently as a powerful archite...
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04/27/2019
Graph Kernels: A Survey
Graph kernels have attracted a lot of attention during the last decade, ...
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04/03/2019
Rep the Set: Neural Networks for Learning Set Representations
In several domains, data objects can be decomposed into sets of simpler ...
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08/07/2018
Message Passing Graph Kernels
Graph kernels have recently emerged as a promising approach for tackling...
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06/06/2018
GraKeL: A Graph Kernel Library in Python
The problem of accurately measuring the similarity between graphs is at ...
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07/29/2017