Federated Learning and Meta Learning: Approaches, Applications, and Directions
Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …
learning (ML) to address resource management, interference management, autonomy, and …
Sparse regularization via convex analysis
I Selesnick - IEEE Transactions on Signal Processing, 2017 - ieeexplore.ieee.org
Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …
regularized least squares, but this method often underestimates the true solution. As an …
Computational methods for large-scale inverse problems: a survey on hybrid projection methods
This paper surveys an important class of methods that combine iterative projection methods
and variational regularization methods for large-scale inverse problems. Iterative methods …
and variational regularization methods for large-scale inverse problems. Iterative methods …
Non-convex sparse regularization via convex optimization for impact force identification
J Liu, B Qiao, Y Wang, W He, X Chen - Mechanical Systems and Signal …, 2023 - Elsevier
Instead of traditional Tikhonov regularization, sparse regularization methods like ℓ 1
regularization have been a popular choice for impact force identification because it can …
regularization have been a popular choice for impact force identification because it can …
Inexact-ADMM based federated meta-learning for fast and continual edge learning
In order to meet the requirements for performance, safety, and latency in many IoT
applications, intelligent decisions must be made right here right now at the network edge …
applications, intelligent decisions must be made right here right now at the network edge …
Majorization–minimization generalized Krylov subspace methods for – optimization applied to image restoration
A new majorization–minimization framework for ℓ _p ℓ p–ℓ _q ℓ q image restoration is
presented. The solution is sought in a generalized Krylov subspace that is build up during …
presented. The solution is sought in a generalized Krylov subspace that is build up during …
Synthesis versus analysis priors via generalized minimax-concave penalty for sparsity-assisted machinery fault diagnosis
Sparse priors for signals play a key role in sparse signal modeling, and sparsity-assisted
signal processing techniques have been studied widely for machinery fault diagnosis. In this …
signal processing techniques have been studied widely for machinery fault diagnosis. In this …
Federated and meta learning over non-wireless and wireless networks: A tutorial
In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …
resource management, interference management, autonomy, and decision-making …
Sparsity-based signal extraction using dual Q-factors for gearbox fault detection
Early detection of faults developed in gearboxes is of great importance to prevent
catastrophic accidents. In this paper, a sparsity-based feature extraction method using the …
catastrophic accidents. In this paper, a sparsity-based feature extraction method using the …
Sparsity-inducing nonconvex nonseparable regularization for convex image processing
A popular strategy for determining solutions to linear least-squares problems relies on using
sparsity-promoting regularizers and is widely exploited in image processing applications …
sparsity-promoting regularizers and is widely exploited in image processing applications …