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
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
Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
Weize Zhang, Mohammed Elmahgiubi, Kasra Rezaee, Behzad Khamidehi, Hamidreza Mirkhani, Fazel Arasteh, Chunlin Li, Muhammad Ahsan Kaleem, Eduardo R. Corral-Soto, Dhruv Sharma, Tongtong Cao
An Analysis of the Preferences of Distribution Indicators in Evolutionary Multi-Objective Optimization
Jesús Guillermo Falcón-Cardona, Mahboubeh Nezhadmoghaddam, Emilio Bernal-Zubieta
An Analysis of Linear Time Series Forecasting Models
William Toner, Luke Darlow