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20152022
most citedStability of Graph Scattering Transforms

36 citations · 206 across the 41 of their papers we have counts for

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Showing 2022Show all

12 papers · 1 filter

eess.SP2022

Convolutional Filtering on Sampled Manifolds

Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro

The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds. The building block for information…

eess.SP2022

Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back

Claudio Battiloro, Zhiyang Wang, Hans Riess +2

In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator. We use the convolution to define tan…

cs.MA20222 cited

Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks

Elijah S. Lee, Lifeng Zhou, Alejandro Ribeiro +1

We consider the problem of finding decentralized strategies for multi-agent perimeter defense games. In this work, we design a graph neural network-based learning framework to lear…

eess.SP2022

Algebraic Convolutional Filters on Lie Group Algebras

Harshat Kumar, Alejandro Parada-Mayorga, Alejandro Ribeiro

Group convolutional neural networks are a useful tool for utilizing symmetries known to be in a signal; however, they require that the signal is defined on the group itself. Existi…

cs.LG20221 cited

Predicting Brain Age using Transferable coVariance Neural Networks

Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1

The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven…

eess.SP2022

Learning with Multigraph Convolutional Filters

Landon Butler, Alejandro Parada-Mayorga, Alejandro Ribeiro

In this paper, we introduce a convolutional architecture to perform learning when information is supported on multigraphs. Exploiting algebraic signal processing (ASP), we propose…