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
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology
Emma Harvey, Hauke Sandhaus, Abigail Z. Jacobs, Emanuel Moss, Mona Sloane
Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm
Valérian Jacques-Dumas, René M. van Westen, Henk A. Dijkstra
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
Jukka I. Ahonen, Nam Le, Honglei Zhang, Antti Hallapuro, Francesco Cricri, Hamed Rezazadegan Tavakoli, Miska M. Hannuksela, Esa Rahtu
Bridging the gap between image coding for machines and humans
Nam Le, Honglei Zhang, Francesco Cricri, Ramin G. Youvalari, Hamed Rezazadegan Tavakoli, Emre Aksu, Miska M. Hannuksela, Esa Rahtu
Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments
Oliver Anton, Victoria A. Henderson, Elisa Da Ros, Ivan Sekulic, Sven Burger, Philipp-Immanuel Schneider, Markus Krutzik
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game
Ardavan S. Nobandegani, Irina Rish, Thomas R. Shultz
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise
Rebecca M. Neeser, Bruno Correia, Philippe Schwaller