Application Proficiency
Application proficiency focuses on optimizing the performance and efficiency of algorithms and models across diverse applications, aiming to improve accuracy, speed, and resource utilization. Current research emphasizes developing robust methods for handling model uncertainties and constraints, often employing Bayesian optimization, metaheuristics, and deep learning architectures like convolutional neural networks and transformers. This field is crucial for advancing various domains, from real-time control systems and fraud detection to personalized medicine and environmental monitoring, by enabling the effective deployment of sophisticated computational tools.
Papers
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
Peichao Li, Muhammad Asad, Conor Horgan, Oscar MacCormac, Jonathan Shapey, Tom Vercauteren
On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks
Matheus Moraes Hammes, Antonio Robles-Kelly
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Sin-Yee Yap, Junn Yong Loo, Chee-Ming Ting, Fuad Noman, Raphael C. -W. Phan, Adeel Razi, David L. Dowe
Multilevel Objective-Function-Free Optimization with an Application to Neural Networks Training
S. Gratton, A. Kopanicakova, Ph. L. Toint
An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection
Ritayu Nagpal, Sam Long, Shahid Jahagirdar, Weiwei Liu, Scott Fazackerley, Ramon Lawrence, Amritpal Singh
Atrial Fibrillation Detection Using RR-Intervals for Application in Photoplethysmographs
Georgia Smith, Yishi Wang