196 citations · 359 across the 21 of their papers we have counts for
8 papers · 1 filter
Transferability Properties of Graph Neural Networks
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provab…
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…
Iterative Decoding for Compositional Generalization in Transformers
Luana Ruiz, Joshua Ainslie, Santiago Ontañón
Deep learning models generalize well to in-distribution data but struggle to generalize compositionally, i.e., to combine a set of learned primitives to solve more complex tasks. I…
Training Stable Graph Neural Networks Through Constrained Learning
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNN) rely on graph convolutions to learn features from network data. GNNs are stable to different types of perturbations of the underlying graph, a property…
Stability to Deformations of Manifold Filters and Manifold Neural Networks
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
The paper defines and studies manifold (M) convolutional filters and neural networks (NNs). \emph{Manifold} filters and MNNs are defined in terms of the Laplace-Beltrami operator e…
Learning by Transference: Training Graph Neural Networks on Growing Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaningful feature representations from network data. However, on large-scale graphs co…
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…