As modern computing becomes limited by energy consumption, there is growing interest in physical computing paradigms that can operate closer to fundamental thermodynamic limits. Thermodynamic ...
Abstract: A fast gradient-descent (FGD) method is proposed for far-field pattern synthesis of large antenna arrays. Compared with conventional gradient-descent (GD) methods for pattern synthesis where ...
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 ...
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Abstract: Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental ...
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 ...
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