DCU Insight AQ
DCU Insight AQ is not a defined scientific topic or project readily identifiable in the provided abstracts. The abstracts cover a broad range of research using Large Language Models (LLMs) and other machine learning techniques across diverse fields, including legal reasoning, medical diagnosis, materials science, and anomaly detection. Current research focuses on improving LLM performance through techniques like multi-agent frameworks, multimodal data integration, and careful data curation, as well as addressing challenges such as hallucinations, bias, and efficient model training. These advancements have the potential to significantly improve data analysis, automate complex tasks, and enhance decision-making across numerous scientific and industrial domains.
Papers
The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights
Khalil Khan, Farhan Ullah, Ikram Syed, Irfan Ullah
The unbearable lightness of Restricted Boltzmann Machines: Theoretical Insights and Biological Applications
Giovanni di Sarra, Barbara Bravi, Yasser Roudi
A precise asymptotic analysis of learning diffusion models: theory and insights
Hugo Cui, Cengiz Pehlevan, Yue M. Lu
Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights
Sy-Tuyen Ho, Tuan Van Vo, Somayeh Ebrahimkhani, Ngai-Man Cheung
Causal Machine Learning Methods for Estimating Personalised Treatment Effects -- Insights on validity from two large trials
Hongruyu Chen, Helena Aebersold, Milo Alan Puhan, Miquel Serra-Burriel