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
A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection
Dominik Kus, Eric Wagner, Jan Pennekamp, Konrad Wolsing, Ina Berenice Fink, Markus Dahlmanns, Klaus Wehrle, Martin Henze
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus
Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan
Nicole N. Lønfeldt, Flavia D. Frumosu, A. -R. Cecilie Mora-Jensen, Nicklas Leander Lund, Sneha Das, A. Katrine Pagsberg, Line K. H. Clemmensen
Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy
Alexander Brenner, Michael Fujarski, Tobias Warnecke, Julian Varghese