Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

In recent years, machine learning (ML) algorithms have proved themselves to be remarkably useful in helping people deal with different tasks: data classification and clustering, pattern revealing, ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine-learning model might think it is a new data point. In computer science ...

A study published in JAMA Network Open describes the utility of multi-level machine learning models in estimating the risk of delay between cancer diagnosis and treatment initiation in a large group ...

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

Periodic maintenance is common too, but still inefficient and often based on time, not actual machine condition. That ...

The varied topography of the Western United States—a patchwork of valleys and mountains, basins and plateaus—results in minutely localized weather. Accordingly, snowfall forecasts for the mountain ...