
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Graph neural networks (GNNs) are processing architectures that exploit g...
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Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)
Spherical signals are useful mathematical models for data arising in man...
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Resource Allocation via ModelFree Deep Learning in Free Space Optical Networks
This paper investigates the general problem of resource allocation for m...
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Balancing Rates and Variance via Adaptive BatchSize for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochasti...
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Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
This paper investigates the optimal resource allocation in free space op...
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Wide and Deep Graph Neural Networks with Distributed Online Learning
Graph neural networks (GNNs) learn representations from network data wit...
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Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph da...
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Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
Radio on Free Space Optics (RoFSO), as a universal platform for heteroge...
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Two Examples of ConvexProgrammingBased HighDimensional Econometric Estimators
Economists specify highdimensional models to address heterogeneity in e...
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Zhan Gao
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