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Found 9 papers, 1 papers with code
Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation
This paper aims to introduce a new statistical learning technique based on sparsity promoting for data-driven modeling and control of solar photovoltaic (PV) systems.
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models.
Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent
The significant practical advantages of the HB method for learning problems are well known, but the question of reducing communications has not been addressed.
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting
Compared to the classical ADMM, a key feature of OADMM is that transmissions are ordered among workers at each iteration such that a worker with the most informative data broadcasts its local variable to neighbors first, and neighbors who have not transmitted yet can update their local variables based on that received transmission.
Distributed Learning With Sparsified Gradient Differences
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data.
Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model
The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic.
Sparse Representation based Multi-sensor Image Fusion: A Review
As a result of several successful applications in computer vision and image processing, sparse representation (SR) has attracted significant attention in multi-sensor image fusion.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.
Information Theory Information Theory
