Abstract: We propose an adaptive moment estimation (Adam)-based $2^{\mathrm {nd}}$ -order Volterra nonlinear equalizer (VNLE) employing a mini-batch gradient descent (MGD) algorithm for intensity ...

Abstract: We propose an adaptive moment estimation (Adam)-based 2 nd-order Volterra nonlinear equalizer (VNLE) employing a mini-batch gradient descent (MGD) algorithm for intensity ...

For over 5 years, Arthur has been professionally covering video games, writing guides and walkthroughs. His passion for video games began at age 10 in 2010 when he first played Gothic, an immersive ...

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini Batch ...

Higher order networks (HONs) extend the traditional pairwise interactions between nodes to higher order interactions involving three or more nodes. This allows for the modeling of more complex ...

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 modern machine learning, optimization algorithms are crucial; they steer the training process by skillfully navigating through complex, high-dimensional loss landscapes. Among these, stochastic ...

Performing gradient descent for calculating slope and intercept of linear regression using sum square residual or mean square error loss function. A "from-scratch" 2 ...

Have you ever wondered how Netflix knows what shows to recommend, or how Siri understands your voice commands? Behind these technological marvels lies a fundamental algorithm called gradient descent — ...

Instead of considering the entire dataset, the Stochastic Gradient Descent (SGD) algorithm processes one random data record at a time to optimize the weights and bias parameters. This method is very ...