36 citations · 206 across the 41 of their papers we have counts for
7 papers · 1 filter
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 Graph Convolutional Neural Networks to Stochastic Perturbations
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
Graph convolutional neural networks (GCNNs) are nonlinear processing tools to learn representations from network data. A key property of GCNNs is their stability to graph perturbat…
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Zhan Gao, Subhrajit Bhattacharya, Leiming Zhang +3
Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer f…
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks
Ting-Kuei Hu, Fernando Gama, Tianlong Chen +4
In this paper, we present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-contro…
Sufficiently Accurate Model Learning for Planning
Clark Zhang, Santiago Paternain, Alejandro Ribeiro
Data driven models of dynamical systems help planners and controllers to provide more precise and accurate motions. Most model learning algorithms will try to minimize a loss funct…