Santa Fe Institute

A simple baseline for AI forecasting in machine learning

In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...
Tech Xplore

Compression technique makes AI models leaner and faster while they're still learning

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational ...

New AI method flags fluid flow tipping points before simulations break down

David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to ...
IEEE

Learning-Based Model Predictive Control With High-Probability Safety Using Gaussian Mixture Models

Abstract: This article introduces a learning-based model predictive control (MPC) framework that leverages Gaussian mixture models (GMMs) to address dynamic system uncertainties effectively. To ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...
Scientific Research Publishing

Rasmussen, C.E. and Williams, C.K.I. (2005) Gaussian Processes for Machine Learning. The MIT Press.

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
C&EN

Integrating Machine Learning into Free Energy Perturbation Workflows

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Free energy perturbation (FEP) methods are among the most accurate tools in ...
Scientific Research Publishing

Molnar, C. (2022) Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, Self-Published.

ABSTRACT: We consider various tasks of recognizing properties of DRSs (Decision Rule Systems) in this paper. As solution algorithms, DDTs (Deterministic Decision Trees) and NDTs (Nondeterministic ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...