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 machine learning pipeline for automated insect monitoring
Aditya Jain, Fagner Cunha, Michael Bunsen, Léonard Pasi, Anna Viklund, Maxim Larrivée, David Rolnick
Predicting the energetic proton flux with a machine learning regression algorithm
Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Evaluating Evidence Attribution in Generated Fact Checking Explanations
Rui Xing, Timothy Baldwin, Jey Han Lau
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines
Honglei Zhang, Jukka I. Ahonen, Nam Le, Ruiying Yang, Francesco Cricri
Certified ML Object Detection for Surveillance Missions
Mohammed Belcaid, Eric Bonnafous, Louis Crison, Christophe Faure, Eric Jenn, Claire Pagetti
Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams
Aditya Gunturu, Yi Wen, Nandi Zhang, Jarin Thundathil, Rubaiat Habib Kazi, Ryo Suzuki
Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
Akshansh Mishra