MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...
bCentre for Translational Bioinformatics, William Harvey Research Institute, London, UK cExperimental Medicine and Rheumatology, William Harvey Research Institute, London, UK dSchool of Infection, ...
Abstract: Competency-based education (CBE) in higher education demands interpretable and scalable tools to monitor student progress. Current studies on CBE have used small samples in short evaluation ...
Machine learning is increasingly applied in environmental chemistry for contaminant screening and property prediction, yet consistent benchmarks are lacking. We compared eight graph neural networks ...
10 The George Institute for Global Health, School of Public Health, Imperial College London, London, UK Background Cardiovascular risk is underassessed in women. Many women undergo screening ...
mlip is a Python library for training and deploying Machine Learning Interatomic Potentials (MLIP) written in JAX. It provides the following functionality: 🎙️ For further information on the design ...
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 ...
Abstract: This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in ...
Background: High-risk chest pain is a critical presentation in emergency departments, frequently indicative of life-threatening cardiopulmonary conditions. Rapid and accurate diagnosis is pivotal for ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback