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
The untapped potential of electrically-driven phase transition actuators to power innovative soft robot designs
Diogo Fonseca, Pedro Neto
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
Yujia Zhou, Zheng Liu, Zhicheng Dou
Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision
Yueyang Cang, Yu hang liu, Li Shi
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
Aymane El Firdoussi, Mohamed El Amine Seddik, Soufiane Hayou, Reda Alami, Ahmed Alzubaidi, Hakim Hacid
VOVTrack: Exploring the Potentiality in Videos for Open-Vocabulary Object Tracking
Zekun Qian, Ruize Han, Junhui Hou, Linqi Song, Wei Feng