Luana Ruiz | DeepAI

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Alejandro Ribeiro
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Santiago Ontañón
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Elvin Isufi
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Joshua Ainslie
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Luiz F. O. Chamon
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Antonio G. Marques
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Zhiyang Wang
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Alejandro Parada-Mayorga
9 publications

∙ 05/29/2023

Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs

This paper studies the relationship between a graph neural network (GNN)...

0 Zhiyang Wang, et al. ∙

∙ 01/25/2023

Graph Neural Tangent Kernel: Convergence on Large Graphs

Graph neural networks (GNNs) achieve remarkable performance in graph mac...

0 Sanjukta Krishnagopal, et al. ∙

∙ 11/20/2022

Convolutional Filtering on Sampled Manifolds

The increasing availability of geometric data has motivated the need for...

0 Zhiyang Wang, et al. ∙

∙ 11/06/2022

Graph Neural Networks for Community Detection on Sparse Graphs

Spectral methods provide consistent estimators for community detection i...

0 Luana Ruiz, et al. ∙

∙ 10/27/2022

Training Graph Neural Networks on Growing Stochastic Graphs

Graph Neural Networks (GNNs) rely on graph convolutions to exploit meani...

0 Juan Cervino, et al. ∙

∙ 10/01/2022

Convolutional Neural Networks on Manifolds: From Graphs and Back

Geometric deep learning has gained much attention in recent years due to...

0 Zhiyang Wang, et al. ∙

∙ 12/09/2021

Transferability Properties of Graph Neural Networks

Graph neural networks (GNNs) are deep convolutional architectures consis...

0 Luana Ruiz, et al. ∙

∙ 10/10/2021

Stability of Neural Networks on Manifolds to Relative Perturbations

Graph Neural Networks (GNNs) show impressive performance in many practic...

0 Zhiyang Wang, et al. ∙

∙ 10/08/2021

Iterative Decoding for Compositional Generalization in Transformers

Deep learning models do well at generalizing to in-distribution data but...

0 Luana Ruiz, et al. ∙

∙ 10/07/2021

Training Stable Graph Neural Networks Through Constrained Learning

Graph Neural Networks (GNN) rely on graph convolutions to learn features...

0 Juan Cervino, et al. ∙

∙ 06/07/2021

Stability of Manifold Neural Networks to Deformations

Stability is an important property of graph neural networks (GNNs) which...

0 Zhiyang Wang, et al. ∙

∙ 06/07/2021

Increase and Conquer: Training Graph Neural Networks on Growing Graphs

Graph neural networks (GNNs) use graph convolutions to exploit network i...

0 Juan Cervino, et al. ∙

∙ 10/27/2020

Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) learn compositional represen...

0 Luana Ruiz, et al. ∙

∙ 10/23/2020

Graph and graphon neural network stability

Graph neural networks (GNNs) are learning architectures that rely on kno...

0 Luana Ruiz, et al. ∙

∙ 08/04/2020

Graph Neural Networks: Architectures, Stability and Transferability

Graph Neural Networks (GNNs) are information processing architectures fo...

0 Luana Ruiz, et al. ∙

∙ 06/05/2020

Graphon Neural Networks and the Transferability of Graph Neural Networks

Graph neural networks (GNNs) rely on graph convolutions to extract local...

0 Luana Ruiz, et al. ∙

∙ 03/03/2020

Graphon Pooling in Graph Neural Networks

Graph neural networks (GNNs) have been used effectively in different app...

0 Alejandro Parada-Mayorga, et al. ∙

∙ 02/03/2020

Gated Graph Recurrent Neural Networks

Graph processes exhibit a temporal structure determined by the sequence ...

0 Luana Ruiz, et al. ∙

∙ 03/29/2019

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Graph signals are signals with an irregular structure that can be descri...

0 Luana Ruiz, et al. ∙

∙ 03/05/2019

Gated Graph Convolutional Recurrent Neural Networks

Graph processes model a number of important problems such as identifying...

0 Luana Ruiz, et al. ∙

∙ 10/29/2018

Median activation functions for graph neural networks

Graph neural networks (GNNs) have been shown to replicate convolutional ...

0 Luana Ruiz, et al. ∙

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