A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures.

Self-organizing maps, and the machine learning protocol involved in creating them, have been in use since the 1980s, Lawrence ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting?

Research shows how artificial intelligence is revolutionizing plastics manufacturing through material development and process ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Machine learning researchers using MLX will benefit from speed improvements in macOS Tahoe 26.2, including support for the M5 GPU-based neural accelerators and Thunderbolt 5 clustering.

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

The project will build upon CSIRO’s expertise in the field of QML to develop new and innovative QML models. QML has the potential to offer enhanced reliability, training speed-up and unique feature ...

The application for admission to the class beginning in Fall 2026 will open on September 1, 2025. Candidates in this admission cycle will apply to enter the program in the Fall Quarter of 2026 and ...

Machine learning is increasingly recognized as a pivotal tool in the evolution of cardiovascular medicine, promising to ...

In both cases, it would be better to train the machine learning model with a loss function that ignores the human’s objective and then adjust predictions ex post according to that objective. We ...