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 and Visualization of Musical Structure using Networks
Alberto Alcalá-Alvarez, Pablo Padilla-Longoria
Mixing Paint: An analysis of color value transformations in multiple coordinate spaces using multivariate linear regression
Alexander Messick
AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
Natalia Grigoriadou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis
Na Li, Thomas Bailleux, Zied Bouraoui, Steven Schockaert
Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units
Biswesh Mohapatra, Seemab Hassan, Laurent Romary, Justine Cassell
How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation
Lixi Zhu, Xiaowen Huang, Jitao Sang