Visual Studio Magazine

Support Vector Regression with SGD Training Using C#

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

Black-box optimization weather intervention method supports future disaster mitigation

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...
Tech Times

ICML 2026 Opens in Seoul Next Week: Record 23,918 Submissions Signal AI Agent Safety Era

ICML 2026 opens in Seoul on July 6 with a record 23,918 submissions — more than double last year — and a research program ...
The Manila Times

WiMi Develops Quantum Convolutional Neural Network Model for Classical Data Classification

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ('WIMI' or the 'Company'), a leading global Hologram Augmented Reality ('AR') Technology provider, has completed systematic benchmark testing on fully ...
IEEE

Distributed Randomized Gradient-Free Mirror Descent Algorithm for Constrained Optimization

Abstract: This article is concerned with the multiagent optimization problem. A distributed randomized gradient-free mirror descent (DRGFMD) method is developed by introducing a randomized ...
Phys.org

Physics of foam strangely resembles AI training

Foams are everywhere: soap suds, shaving cream, whipped toppings and food emulsions like mayonnaise. For decades, scientists believed that foams behave like glass, their microscopic components trapped ...
Hosted on MSN

Mini-batch gradient descent in deep learning explained

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 ...
Hosted on MSN

Linear regression using gradient descent explained simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
Scientific Research Publishing

Ruder, S. (2016) An Overview of Gradient Descent Optimization Algorithms. arXiv Preprint.

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 ...
IEEE

Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-Max Optimization

Abstract: Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this ...
Quanta Magazine

Three Hundred Years Later, a Tool From Isaac Newton Gets an Update

Every day, researchers search for optimal solutions. They might want to figure out where to build a major airline hub. Or to determine how to maximize return while minimizing risk in an investment ...