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06/27/2023
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Non-asymptotic convergence analysis of quasi-Newton methods has gained a...
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06/24/2023
Differentially Private Decentralized Deep Learning with Consensus Algorithms
Cooperative decentralized deep learning relies on direct information exc...
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06/08/2023
Spectrum Sharing between High Altitude Platform Network and Terrestrial Network: Modeling and Performance Analysis
Achieving seamless global coverage is one of the ultimate goals of space...
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05/18/2023
Constrained Environment Optimization for Prioritized Multi-Agent Navigation
Traditional approaches to the design of multi-agent navigation algorithm...
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03/08/2023
Learning Environment-Aware Control Barrier Functions for Safe and Feasible Multi-Robot Navigation
Control Barrier Functions (CBFs) have been applied to provide safety gua...
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02/16/2023
AirGNN: Graph Neural Network over the Air
Graph neural networks (GNNs) are information processing architectures th...
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11/13/2022
Learning Stable Graph Neural Networks via Spectral Regularization
Stability of graph neural networks (GNNs) characterizes how GNNs react t...
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10/30/2022
Decentralized Channel Management in WLANs with Graph Neural Networks
Wireless local area networks (WLANs) manage multiple access points (APs)...
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09/22/2022
Environment Optimization for Multi-Agent Navigation
Traditional approaches to the design of multi-agent navigation algorithm...
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07/18/2022
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation
Traditional fine-grained image classification typically relies on large-...
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01/29/2022
Learning Stochastic Graph Neural Networks with Constrained Variance
Stochastic graph neural networks (SGNNs) are information processing arch...
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07/19/2021
Wide and Deep Graph Neural Network with Distributed Online Learning
Graph neural networks (GNNs) are naturally distributed architectures for...
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06/19/2021
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations
Graph convolutional neural networks (GCNNs) are nonlinear processing too...
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06/05/2021
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Graph neural networks (GNNs) are processing architectures that exploit g...
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10/12/2020
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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07/27/2020
Resource Allocation via Model-Free Deep Learning in Free Space Optical Networks
This paper investigates the general problem of resource allocation for m...
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07/02/2020
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochasti...
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06/26/2020
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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06/11/2020
Wide and Deep Graph Neural Networks with Distributed Online Learning
Graph neural networks (GNNs) learn representations from network data wit...
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06/04/2020
Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph da...
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06/21/2019
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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06/27/2018
