This paper addresses a critical challenge in Industry 4.0 robotics by enhancing Visual Inertial Odometry (VIO) systems to operate effectively in dynamic and low-light industrial environments, which ...

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 ...

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 ...

The spatial positioning of magnetic resonance imaging (MRI) images is determined by generating a linearly varying gradient magnetic field through a gradient coil, which plays a pivotal role in the ...

Struggling to understand how logistic regression works with gradient descent? This video breaks down the full mathematical derivation step-by-step, so you can truly grasp this core machine learning ...

Abstract: This article studies agent-server system identification problems by using a varying infimum gradient descent (VI-GD) algorithm. To efficiently use the GD algorithm for the agent-server with ...

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 ...

This repository contains the official PyTorch implementation for Grams optimizer. We introduce Gradient Descent with Adaptive Momentum Scaling (Grams), a novel optimization algorithm that decouples ...

This paper proposes a family of line-search methods to deal with weighted orthogonal procrustes problems. In particular, the proposed family uses a search direction based on a convex combination ...

Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...