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
Towards Perspective-Based Specification of Machine Learning-Enabled Systems
Hugo Villamizar, Marcos Kalinowski, Helio Lopes
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability
Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, Christian Janiesch
Machine Learning-Driven Process of Alumina Ceramics Laser Machining
Razyeh Behbahani, Hamidreza Yazdani Sarvestani, Erfan Fatehi, Elham Kiyani, Behnam Ashrafi, Mikko Karttunen, Meysam Rahmat
Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction
David E. Ruíz-Guirola, Carlos A. Rodríguez-López, Samuel Montejo-Sánchez, Richard Demo Souza, Onel L. A. López, Hirley Alves
A universal synthetic dataset for machine learning on spectroscopic data
Jan Schuetzke, Nathan J. Szymanski, Markus Reischl