Full Potential
"Full potential" research explores maximizing the capabilities of various models and algorithms across diverse fields. Current efforts focus on improving model performance in tasks like program repair, multimodal search, and medical image segmentation, often leveraging large language models (LLMs), diffusion models, and graph neural networks. This research is significant because it aims to enhance the efficiency and accuracy of existing technologies, leading to advancements in areas such as software development, AI-assisted content creation, and healthcare diagnostics. The ultimate goal is to unlock the full capabilities of these models for practical applications and scientific discovery.
Papers
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede, Pouria Rouzrokh, Cooper U. Gamble, Hari M. Trivedi, Cody C. Wyles, Andrew B. Sellergren, Saptarshi Purkayastha, Bradley J. Erickson, Judy W. Gichoya
Exploring the Potential of Large Language Models in Computational Argumentation
Guizhen Chen, Liying Cheng, Luu Anh Tuan, Lidong Bing