A marriage of formal methods and LLMs seeks to harness the strengths of both.

Abstract: Most machine learning methods need abundant training and testing datasets to perform well. In reality, data may be limited due to time constraints or other practical reasons. In such ...

Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Additionally, the effects of social media platform type, machine learning approach, and use of outcome measures in depression prediction models need attention. Analyzing social media texts for ...

Abstract: Deep learning or machine learning using image input is a good field in medical image analysis that is rapidly evolving. Machine learning is expected to become the norm in the field of ...

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...

Division of Applied Chemistry, Faculty of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan ...

Powerful and practical machine learning tools for machine vision applications are already available to everyone, even if you’re not a data scientist. It might come as a bit of a surprise, but machine ...

For centuries, the El Niño Southern Oscillation (ENSO) has played havoc with global weather. The climate event repeats every few years and can trigger droughts, floods, and hurricanes, but it is ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...