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
Analysis of Tomographic Reconstruction of 2D Images using the Distribution of Unknown Projection Angles
Sheel Shah, Karthik S. Gurumoorthy, Ajit Rajwade
Deep state-space modeling for explainable representation, analysis, and generation of professional human poses
Brenda Elizabeth Olivas-Padilla, Alina Glushkova, Sotiris Manitsaris
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis
On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms
Ricardo Trancoso, Ruben Queiros, Helder Fontes, Rui Campos