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
1D Convolutional neural networks and machine learning algorithms for spectral data classification with a case study for Covid-19
Breno Aguiar Krohling, Renato A Krohling
Quadruple-star systems are not always nested triples: a machine learning approach to dynamical stability
Pavan Vynatheya, Rosemary A. Mardling, Adrian S. Hamers
Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions
Shradha Verma, Tripti Goel, M Tanveer, Weiping Ding, Rahul Sharma, R Murugan
Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes
Mohammad Vahid Jamali, Xiyang Liu, Ashok Vardhan Makkuva, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath