It’s happened to all of us: you find the perfect model for your needs — a bracket, a box, a cable clip, but it only comes in ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. Exclusive-DOGE 'doesn't exist' with eight months left on ...

Abstract: Spatially varying degradation is related to real-world problems, such medical, astronomical, underwater or metalens imaging, neutron radiography, motion blur, optical remote sensing, etc.

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

Abstract: Selecting an appropriate step size is critical in Gradient Descent algorithms used to train Neural Networks for Deep Learning tasks. A small value of the step size leads to slow convergence, ...