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
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor
Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration
Tan Chen, Zhe Huang, James Motes, Junyi Geng, Quang Minh Ta, Holly Dinkel, Hameed Abdul-Rashid, Jessica Myers, Ye-Ji Mun, Wei-che Lin, Yuan-yung Huang, Sizhe Liu, Marco Morales, Nancy M. Amato, Katherine Driggs-Campbell, Timothy Bretl
Insights into the origin of halo mass profiles from machine learning
Luisa Lucie-Smith, Susmita Adhikari, Risa H. Wechsler
Insights on Modelling Physiological, Appraisal, and Affective Indicators of Stress using Audio Features
Andreas Triantafyllopoulos, Sandra Zänkert, Alice Baird, Julian Konzok, Brigitte M. Kudielka, Björn W. Schuller
Augmentations: An Insight into their Effectiveness on Convolution Neural Networks
Sabeesh Ethiraj, Bharath Kumar Bolla