3 citations · 3 across the 6 of their papers we have counts for
17 papers
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…
Training Graph Neural Networks on Growing Stochastic Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational…
Convolutional Neural Networks on Manifolds: From Graphs and Back
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Geometric deep learning has gained much attention in recent years due to more available data acquired from non-Euclidean domains. Some examples include point clouds for 3D models a…
Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks
Zhiyang Wang, Luana Ruiz, Mark Eisen +1
We consider the problem of resource allocation in large scale wireless networks. When contextualizing wireless network structures as graphs, we can model the limits of very large w…
Stability of Neural Networks on Manifolds to Relative Perturbations
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNNs) show impressive performance in many practical scenarios, which can be largely attributed to their stability properties. Empirically, GNNs can scale wel…
Stability of Neural Networks on Riemannian Manifolds
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Convolutional Neural Networks (CNNs) have been applied to data with underlying non-Euclidean structures and have achieved impressive successes. This brings the stability analysis o…