Selecting appropriate machine learning (ML) methods for domain-specific tasks remains a persistent challenge, particularly in medicine where datasets are often small, heterogeneous, and incomplete.

Treble Technologies, the pioneer in cloud-based acoustic simulation and synthetic audio data generation, and Hugging Face, ...

An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting ...

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 ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Samuel Kaski’s two-part research lab in ELLIS Institute Finland (Probabilistic Machine Learning, Aalto University) and the Centre for AI Fundamentals in University of Manchester, is searching for ...