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
Machine Learning-Based Multi-Objective Design Exploration Of Flexible Disc Elements
Gehendra Sharma, Sungkwang Mun, Nayeon Lee, Luke Peterson, Daniela Tellkamp, Anand Balu Nellippallil
Machine Perception-Driven Image Compression: A Layered Generative Approach
Yuefeng Zhang, Chuanmin Jia, Jiannhui Chang, Siwei Ma