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 Completions Sequencing for Well Performance Optimization
Anjie Liu, Jinglang W. Sun, Anh Ngo, Ademide O. Mabadeje, Jose L. Hernandez-Mejia
Statistical Agnostic Regression: a machine learning method to validate regression models
Juan M Gorriz, J. Ramirez, F. Segovia, F. J. Martinez-Murcia, C. Jiménez-Mesa, J. Suckling
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton
Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques
José Alberto Benítez-Andrades, María Teresa García-Ordás, María Álvarez-González, Raquel Leirós-Rodríguez, Ana F López Rodríguez
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
J. Senthilnath, Adithya Bhattiprolu, Ankur Singh, Bangjian Zhou, Min Wu, Jón Atli Benediktsson, Xiaoli Li
RAGE for the Machine: Image Compression with Low-Cost Random Access for Embedded Applications
Christian D. Rask, Daniel E. Lucani
cecilia: A Machine Learning-Based Pipeline for Measuring Metal Abundances of Helium-rich Polluted White Dwarfs
M. Badenas-Agusti, J. Viaña, A. Vanderburg, S. Blouin, P. Dufour, S. Xu, L. Sha
Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways
Angelina Wang, Xuechunzi Bai, Solon Barocas, Su Lin Blodgett
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang, Xiang Ren