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
Using machine learning to inform harvest control rule design in complex fishery settings
Felipe Montealegre-Mora, Carl Boettiger, Carl J. Walters, Christopher L. Cahill
Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication
Shekhar Madhav Khairnar, Huu Phong Nguyen, Alexis Desir, Carla Holcomb, Daniel J. Scott, Ganesh Sankaranarayanan
Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
Sheikh Junaid Fayaz, Nestor Montiel-Bohorquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan
Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
Ines Boujnah, Nehal Afifi, Andreas Wettstein, Sven Matthiesen
dsLassoCov: a federated machine learning approach incorporating covariate control
Han Cao, Augusto Anguita, Charline Warembourg, Xavier Escriba-Montagut, Martine Vrijheid, Juan R. Gonzalez, Tim Cadman, Verena Schneider-Lindner, Daniel Durstewitz, Xavier Basagana, Emanuel Schwarz
Surveying the space of descriptions of a composite system with machine learning
Kieran A. Murphy, Yujing Zhang, Dani S. Bassett
Machine learning-based classification for Single Photon Space Debris Light Curves
Nadine M. Trummer, Amit Reza, Michael A. Steindorfer, Christiane Helling
Using Machine Bias To Measure Human Bias
Wanxue Dong, Maria De-Arteaga, Maytal Saar-Tsechansky