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
Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses
Gaurav Kumar Gupta, Aditi Singh, Sijo Valayakkad Manikandan, Abul Ehtesham
Exploring the Potential of Human-LLM Synergy in Advancing Qualitative Analysis: A Case Study on Mental-Illness Stigma
Han Meng, Yitian Yang, Yunan Li, Jungup Lee, Yi-Chieh Lee
Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems
Alejandro Garcia-Sosa, Jose J. Quintana-Hernandez, Miguel A. Ferrer Ballester, Cristina Carmona-Duarte
Artificial Intelligence-powered fossil shark tooth identification: Unleashing the potential of Convolutional Neural Networks
Andrea Barucci, Giulia Ciacci, Pietro Liò, Tiago Azevedo, Andrea Di Cencio, Marco Merella, Giovanni Bianucci, Giulia Bosio, Simone Casati, Alberto Collareta
Unveiling the Potential of LLM-Based ASR on Chinese Open-Source Datasets
Xuelong Geng, Tianyi Xu, Kun Wei, Bingshen Mu, Hongfei Xue, He Wang, Yangze Li, Pengcheng Guo, Yuhang Dai, Longhao Li, Mingchen Shao, Lei Xie
Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls
Sicong Liu, Wentao Zhou, Zimu Zhou, Bin Guo, Minfan Wang, Cheng Fang, Zheng Lin, Zhiwen Yu
Revolutionizing Traffic Sign Recognition: Unveiling the Potential of Vision Transformers
Susano Mingwin, Yulong Shisu, Yongshuai Wanwag, Sunshin Huing
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle
Luca Deck, Astrid Schomäcker, Timo Speith, Jakob Schöffer, Lena Kästner, Niklas Kühl
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
Weike Peng, Jiaxin Gao, Yuntian Chen, Shengwei Wang