New Machine
Research on "new machines" broadly encompasses the development and application of machine learning across diverse fields, aiming to improve efficiency, accuracy, and decision-making. Current efforts focus on refining model architectures like convolutional neural networks, gradient boosting machines, and transformers for tasks ranging from image and signal processing to complex prediction and control problems. This research is significant because it drives advancements in various sectors, including healthcare, energy, manufacturing, and transportation, by enabling automated processes, improved diagnostics, and more efficient resource allocation.
Papers
Using machine learning algorithms to determine the post-COVID state of a person by his rhythmogram
Sergey Stasenko, Andrey Kovalchuk, Eremin Evgeny, Natalya Zarechnova, Maria Tsirkova, Sergey Permyakov, Sergey Parin, Sofia Polevaya
Using machine learning algorithms to determine the emotional disadaptation of a person by his rhythmogram
Sergey Stasenko, Olga Shemagina, Eremin Evgeny, Vladimir Yakhno, Sergey Parin, Sofia Polevaya
Learning to Learn: How to Continuously Teach Humans and Machines
Parantak Singh, You Li, Ankur Sikarwar, Weixian Lei, Daniel Gao, Morgan Bruce Talbot, Ying Sun, Mike Zheng Shou, Gabriel Kreiman, Mengmi Zhang
Emerging trends in machine learning for computational fluid dynamics
Ricardo Vinuesa, Steve Brunton