Active learning represents a transformative paradigm in machine learning, aimed at reducing the annotation burden by selectively querying the most informative data points. This approach leverages ...

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has developed a ...

South Korean researchers have developed a guided-learning framework that accurately predicts PV power without requiring irradiance sensors during operation, using routine meteorological data instead.

Scientists have developed a geometric deep learning method that can create a coherent picture of neuronal population activity during cognitive and motor tasks across experimental subjects and ...

Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces. At the ...

Deep learning techniques can enhance diagnosis of Meniere disease (MD) and severity grading, according to a study published ...

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

Rules-based automation (RBA) and learning are two training mechanisms in robotics. While there are many others, these are two ...

Virginia Clinton-Lisell receives funding from the U.S. Department of Education and Hewlett Foundation. Students do better when lessons are tailored to individual learning styles – but not so much that ...