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
Machine learning-based spin structure detection
Isaac Labrie-Boulay, Thomas Brian Winkler, Daniel Franzen, Alena Romanova, Hans Fangohr, Mathias Kläui
Paraphrase Detection: Human vs. Machine Content
Jonas Becker, Jan Philip Wahle, Terry Ruas, Bela Gipp
Derivative-based Shapley value for global sensitivity analysis and machine learning explainability
Hui Duan, Giray Ökten
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala, Alban Farchi, Che Liu, Pierre Tandeo, Ronan Fablet, Didier Lucor, Bertrand Iooss, Julien Brajard, Dunhui Xiao, Tijana Janjic, Weiping Ding, Yike Guo, Alberto Carrassi, Marc Bocquet, Rossella Arcucci
Machine learning for discovering laws of nature
Lizhi Xin, Kevin Xin, Houwen Xin