Build, test, and deploy ML-driven trading strategies — from data sourcing to live execution. This repository hosts the code for Machine Learning for Trading, 3rd Edition by Stefan Jansen — a ground-up ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

Abstract: Hyper parameter optimization (HPO) is a crucial step in modern machine learning systems. Bayesian optimization (BO) has shown great promise in HPO, where the parameter evaluation is ...

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

When Meta broke ground last year on its data center in Rosemount, Minn., about 15 miles south of Minneapolis and St. Paul, the social media giant faced a concrete paradox: the material’s carbon burden ...

Massachusetts Institute of Technology, Department of Chemical Engineering, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States Drexel University, Department of Chemical and ...

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...

Abstract: The application of machine learning (ML) models to forecast microstrip patch antenna characteristics and maximize their performance via Bayesian optimization is presented in this study.

Understanding the interplay between network architecture, dataset statistics, and learning algorithms is a key challenge in deep learning. We overcome this challenge analytically for zero-noise ...