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
CounterQuill: Investigating the Potential of Human-AI Collaboration in Online Counterspeech Writing
Xiaohan Ding, Kaike Ping, Uma Sushmitha Gunturi, Buse Carik, Sophia Stil, Lance T Wilhelm, Taufiq Daryanto, James Hawdon, Sang Won Lee, Eugenia H Rho
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
Muzhi Zhu, Yang Liu, Zekai Luo, Chenchen Jing, Hao Chen, Guangkai Xu, Xinlong Wang, Chunhua Shen
Vision-based Xylem Wetness Classification in Stem Water Potential Determination
Pamodya Peiris, Aritra Samanta, Caio Mucchiani, Cody Simons, Amit Roy-Chowdhury, Konstantinos Karydis
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification
Leire Benito-Del-Valle, Aitor Alvarez-Gila, Itziar Eguskiza, Cristina L. Saratxaga
Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research
Vandita Shukla, Luca Morelli, Pawel Trybala, Fabio Remondino, Wentian Gan, Yifei Yu, Xin Wang
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
Bingyao Liu, Iris Li, Jianhua Yao, Yuan Chen, Guanming Huang, Jiajing Wang
Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs
Clément Christophe, Tathagata Raha, Svetlana Maslenkova, Muhammad Umar Salman, Praveen K Kanithi, Marco AF Pimentel, Shadab Khan
Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization
Yuanzhe He, Yutao Zheng, Shijie Xu, Chang Liu, Di Peng, Yingzheng Liu, Weiwei Cai
ChatGPT's Potential in Cryptography Misuse Detection: A Comparative Analysis with Static Analysis Tools
Ehsan Firouzi, Mohammad Ghafari, Mike Ebrahimi
Learning local and semi-local density functionals from exact exchange-correlation potentials and energies
Bikash Kanungo, Jeffrey Hatch, Paul M. Zimmerman, Vikram Gavini