Abstract: Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental ...

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. FBI releases new details on suspect in Nancy Guthrie ...

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 ...

In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

In the '8_sgd_vs_gd' folder, the 'gd_and_sgd.ipynb' file, there is a logic flaw in the Stochastic Gradient Descent code, Since for SGD, it uses 1 randomly selected ...

Abstract: Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several ...

ABSTRACT: As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as ...

Abstract: We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based ...

This week I interviewed Senator Amy Klobuchar, Democrat of Minnesota, about her Preventing Algorithmic Collusion Act. If you don’t know what algorithmic collusion is, it’s time to get educated, ...