6 papers
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…
Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
Zhan Gao, Mark Eisen, Alejandro Ribeiro
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space op…