The BayesXtreme project will develop advanced AI techniques to help researchers and AI systems understand complex data using fewer resources. Bayesian inference, a powerful statistical method, can analyze noisy and limited data to create reliable models. However, inference can be resource-intensive, consuming significant time and energy. BayesXtreme focuses on "sample-efficient inference", a cutting-edge approach that achieves accurate results faster and with fewer resources. This project aims to improve the flexibility and robustness of the method, develop parallel algorithms, and incorporate insights from deep learning. BayesXtreme will make advanced data analysis more accessible to academia and industry. It will speed up the process and reduce the energy footprint while enhancing the explainability and reliability of AI-assisted data analysis. Ultimately, the project pushes the limits of ICT by maximizing the efficiency of extracting valuable information from data and computation.

2024

2026