Unit-free theorem pinpoints key variables for AI and physics models

Machine learning models are designed to take in data, to find patterns or relationships within those data, and to use what ...
IEEE

A Comparison of Approaches for Handling Concept Drifts in Data Processed With Machine Learning

Abstract: In the realm of machine learning models, the pursuit of achieving favorable metrics is undeniably significant. However, these models confront phenomena that can diminish their effectiveness ...
IEEE

Stability of Machine Learning Predictive Features Under Limited Data

Abstract: In a world where Machine Learning (ML) is increasingly used to make predictions about critical events, such as health outcomes, it is crucial to ensure decision-makers have access to ...
GitHub

Spectral Machine Learning for Market Microstructure: Fourier-Laplace Signal Decomposition for Alpha Discovery

Financial markets are noisy, chaotic systems—but beneath that chaos lie hidden frequency structures: cycles, mean-reversion waves, and volatility bursts that traditional time-domain models often miss.
Frontiers

Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models

Objectives: To investigate the role of retinal characteristics for high myopia (HM) prediction based on multi-machine learning (ML) and to provide an interpretable framework for the results. Methods: ...
TechSpot

Sony and AMD tease PlayStation 6 technology, highlight path tracing and machine learning features

Looking ahead: Prior leaks and rumors regarding the PlayStation 6 primarily focused on AMD's future hardware architectures and their dramatic improvements to ray tracing performance. A brief new ...