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
Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms
Basma Khelfa, Ibrahima Ba, Antoine Tordeux
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie Wood, Ludvig M. Sollid, Victor Greiff, Geir Kjetil Sandve