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Found 11 papers, 0 papers with code
Bayesian Neural Network For Personalized Federated Learning Parameter Selection
Federated learning's poor performance in the presence of heterogeneous data remains one of the most pressing issues in the field.
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
Performance degradation owing to data heterogeneity and low output interpretability are the most significant challenges faced by federated learning in practical applications.
Out of Distribution (OOD) Detection
Personalized Federated Learning
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
The LMS-GNN is a combination of adaptive graph filters and Graph Neural Networks (GNN).
Bayesian Neural Networks: A Min-Max Game Framework
Bayesian neural networks use random variables to describe the neural networks rather than deterministic neural networks and are mostly trained by variational inference which updates the mean and variance at the same time.
Graph Signal Processing For Cancer Gene Co-Expression Network Analysis
In this study, we used graph frequency analysis of cancer genetic signals defined on a co-expression network to describe the spectral properties of underlying cancer systems.
Online Signal Estimation on the Graph Edges via Line Graph Transformation
The processing of signals on graph edges is challenging considering that Graph Signal Processing techniques are defined only on the graph nodes.
Sequential Monte Carlo Graph Convolutional Network for Dynamic Brain Connectivity
An increasingly important brain function analysis modality is functional connectivity analysis which regards connections as statistical codependency between the signals of different brain regions.
Complex Isotropic α-Stable-Rician Model for Heterogeneous SAR Images
This article introduces a novel probability distribution model, namely Complex Isotropic {\alpha}-Stable-Rician (CI{\alpha}SR), for characterizing the data histogram of synthetic aperture radar (SAR) images.
Fast Cauchy-Rician Modelling of SAR Images with Method of Algebraic Moments Estimator
SAR technology has been intensively implemented for geo-sensing and mapping purposes due to its advantages of high azimuthal resolution and weather-independent operation compared to other remote sensing technologies.
Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise
In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise.
Network Pruning Optimization by Simulated Annealing Algorithm
In this work, we reduce network complexity by pruning and structure optimization.
