Abstract: Bayesian optimization is commonly used to optimize black-box functions associated with simulations in engineering and science. Bayesian optimization contains two essential components: the ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. In this paper, we introduce a methodology to improve upon the ...

1 Aviation Service Institute,Jiangsu Aviation Technical College, Zhenjiang, China 2 Ocean College, Jiangsu University of Science and Technology, Zhenjiang, China Reliable forecasting of air cargo ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

This repository contains experiment that implements Bayesian Optimization (BO) techniques for Conditional Value-at-Risk (CVaR)-based portfolio optimization, inspired by the research paper "Bayesian ...

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...

Aqarios' platform Luna v1.0 marks a major milestone in quantum optimization. This release significantly improves usability, performance, and real-world applicability by introducing FlexQAOA, a hybrid ...

ABSTRACT: The rapid proliferation of the Internet of Things (IoT) and Industrial IoT (IIoT) has revolutionized industries through enhanced connectivity and automation. However, this expansion has ...