Preliminary Study
Preliminary studies across diverse scientific fields are exploring the capabilities and limitations of novel models and algorithms. Current research focuses on applying large language models (LLMs), Kolmogorov-Arnold Networks (KANs), and other deep learning architectures to tasks ranging from medical diagnosis and robotic control to soil analysis and cybersecurity. These investigations aim to improve existing methods, address data scarcity issues, and ultimately enhance the accuracy, efficiency, and reliability of various technologies and scientific processes.
Papers
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
Karima Makhlouf, Tamara Stefanovic, Heber H. Arcolezi, Catuscia Palamidessi
Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs
Arpan Dasgupta, Niclas Boehmer, Neha Madhiwalla, Aparna Hedge, Bryan Wilder, Milind Tambe, Aparna Taneja
Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study
Chunlei Xu, Michael Pregesbauer, Naga Sravani Chilukuri, Daniel Windhager, Mahsa Yousefi, Pedro Julian, Lothar Ratschbacher
Exploring Machine Learning Algorithms for Infection Detection Using GC-IMS Data: A Preliminary Study
Christos Sardianos, Chrysostomos Symvoulidis, Matthias Schlögl, Iraklis Varlamis, Georgios Th. Papadopoulos
Automated Report Generation for Lung Cytological Images Using a CNN Vision Classifier and Multiple-Transformer Text Decoders: Preliminary Study
Atsushi Teramoto, Ayano Michiba, Yuka Kiriyama, Tetsuya Tsukamoto, Kazuyoshi Imaizumi, Hiroshi Fujita
Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study
Jinze Zhao, Peihao Wang, Zhangyang Wang