29 citations · 84 across the 14 of their papers we have counts for
6 papers · 1 filter
Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)
Zhan Gao, Fernando Gama, Alejandro Ribeiro
Spherical convolutional neural networks (Spherical CNNs) learn nonlinear representations from 3D data by exploiting the data structure and have shown promising performance in shape…
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems
Zhan Gao, Alec Koppel, Alejandro Ribeiro
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we s…
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications
Zhan Gao, Mark Eisen, Alejandro Ribeiro
This paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. The resource allocation problem…
Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
Zhan Gao, Mark Eisen, Alejandro Ribeiro
This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. The optimal allocation maximizes an average weighted sum-capacity subject to…
Wide and Deep Graph Neural Networks with Distributed Online Learning
Zhan Gao, Fernando Gama, Alejandro Ribeiro
Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oft…
Stochastic Graph Neural Networks
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN arch…