General Analysis
General analysis encompasses a broad range of methodologies applied across diverse scientific domains to extract meaningful insights from data. Current research focuses on developing robust and efficient analytical techniques, including the application of machine learning models like convolutional neural networks, graph neural networks, and transformer architectures, as well as statistical methods for data modeling and hypothesis testing. These advancements are improving the accuracy and efficiency of analyses in fields ranging from medical image processing and materials science to social media analysis and autonomous systems, ultimately leading to more reliable scientific findings and improved decision-making in various applications.
Papers
Who is leading in AI? An analysis of industry AI research
Ben Cottier, Tamay Besiroglu, David Owen
Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
Selina Drews, Michael Kohler
A Survey and Analysis of Evolutionary Operators for Permutations
Vincent A. Cicirello
Regression-Based Analysis of Multimodal Single-Cell Data Integration Strategies
Bhavya Mehta, Nirmit Deliwala, Madhav Chandane
PARK: Parkinson's Analysis with Remote Kinetic-tasks
Md Saiful Islam, Sangwu Lee, Abdelrahman Abdelkader, Sooyong Park, Ehsan Hoque
Analysis of Visual Features for Continuous Lipreading in Spanish
David Gimeno-Gómez, Carlos-D. Martínez-Hinarejos