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
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini
A Unified Framework for U-Net Design and Analysis
Christopher Williams, Fabian Falck, George Deligiannidis, Chris Holmes, Arnaud Doucet, Saifuddin Syed
Modelling, Analysis and Control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV
Youssef Aboudorra, Chiara Gabellieri, Ralph Brantjes, Quentin Sablé, Antonio Franchi
Automating the Analysis of Institutional Design in International Agreements
Anna Wróblewska, Bartosz Pieliński, Karolina Seweryn, Sylwia Sysko-Romańczuk, Karol Saputa, Aleksandra Wichrowska, Hanna Schreiber
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Diederick Vermetten, Manuel López-Ibáñez, Olaf Mersmann, Richard Allmendinger, Anna V. Kononova
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Sohee Yang, Jonghyeon Kim, Joel Jang, Seonghyeon Ye, Hyunji Lee, Minjoon Seo