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-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials
Lingxiao Yuan, Emma Lejeune, Harold S. Park
Can a Machine be Conscious? Towards Universal Criteria for Machine Consciousness
Nur Aizaan Anwar, Cosmin Badea
A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers
Asmar Muqeet, Shaukat Ali, Tao Yue, Paolo Arcaini
Computer aided diagnosis system for Alzheimers disease using principal component analysis and machine learning based approaches
Lilia Lazli
Machine learning-based optimization workflow of the homogeneity of spunbond nonwovens with human validation
Viny Saajan Victor, Andre Schmeißer, Heike Leitte, Simone Gramsch
Machine learning augmented diagnostic testing to identify sources of variability in test performance
Christopher J. Banks, Aeron Sanchez, Vicki Stewart, Kate Bowen, Graham Smith, Rowland R. Kao
MUGC: Machine Generated versus User Generated Content Detection
Yaqi Xie, Anjali Rawal, Yujing Cen, Dixuan Zhao, Sunil K Narang, Shanu Sushmita