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
Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
Mridula Vijendran, Jingjing Deng, Shuang Chen, Edmond S. L. Ho, Hubert P. H. Shum
Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge
Yuhe Ji, Yilun Liu, Feiyu Yao, Minggui He, Shimin Tao, Xiaofeng Zhao, Su Chang, Xinhua Yang, Weibin Meng, Yuming Xie, Boxing Chen, Hao Yang
Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis
Hao Yang, Zhenyu Zhang, Yanyan Zhao, Bing Qin
Analysis of High-dimensional Gaussian Labeled-unlabeled Mixture Model via Message-passing Algorithm
Xiaosi Gu, Tomoyuki Obuchi
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based Approach
Xinyu Yuan, Yan Qiao, Zhenchun Wei, Zeyu Zhang, Minyue Li, Pei Zhao, Rongyao Hu, Wenjing Li
Quantum-enhanced unsupervised image segmentation for medical images analysis
Laia Domingo, Mahdi Chehimi
Application of AI to formal methods -- an analysis of current trends
Sebastian Stock, Jannik Dunkelau, Atif Mashkoor
The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
Marcel Robeer, Michiel Bron, Elize Herrewijnen, Riwish Hoeseni, Floris Bex