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Found 7 papers, 1 papers with code
Robust Fourier Neural Networks
Fourier embedding has shown great promise in removing spectral bias during neural network training.
Stochastic gradient descent for streaming linear and rectified linear systems with Massart noise
We propose SGD-exp, a stochastic gradient descent approach for linear and ReLU regressions under Massart noise (adversarial semi-random corruption model) for the fully streaming setting.
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints
The Kaczmarz method (KZ) and its variants, which are types of stochastic gradient descent (SGD) methods, have been extensively studied due to their simplicity and efficiency in solving linear equation systems.
Federated Gradient Matching Pursuit
In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients.
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence.
Information Theory Numerical Analysis Information Theory Numerical Analysis 94-XX
Polar Deconvolution of Mixed Signals
The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components.
Sub-Gaussian Matrices on Sets: Optimal Tail Dependence and Applications
In many applications, e. g., compressed sensing, this norm may be large, or even growing with dimension, and thus it is important to characterize this dependence.
