Foundation Model
Foundation models are large, pre-trained AI models designed to generalize across diverse tasks and datasets, offering a powerful alternative to task-specific models. Current research emphasizes adapting these models to various domains, including healthcare (e.g., medical image analysis, EEG interpretation), scientific applications (e.g., genomics, weather forecasting), and robotics, often employing architectures like transformers and mixtures of experts with innovative gating functions. This approach promises to improve efficiency and accuracy in numerous fields by leveraging the knowledge embedded within these powerful models, streamlining data analysis and enabling new applications previously hindered by data scarcity or computational limitations.
Papers
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning
Han Yuan, Chuan Hong
A Survey on Robotics with Foundation Models: toward Embodied AI
Zhiyuan Xu, Kun Wu, Junjie Wen, Jinming Li, Ning Liu, Zhengping Che, Jian Tang
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
Fangzhao Zhang, Mert Pilanci
On Catastrophic Inheritance of Large Foundation Models
Hao Chen, Bhiksha Raj, Xing Xie, Jindong Wang
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
Mahdi Beitollahi, Alex Bie, Sobhan Hemati, Leo Maxime Brunswic, Xu Li, Xi Chen, Guojun Zhang
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
Xi Li, Jiaqi Wang
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
Debarun Bhattacharjya, Junkyu Lee, Don Joven Agravante, Balaji Ganesan, Radu Marinescu
A Survey for Foundation Models in Autonomous Driving
Haoxiang Gao, Zhongruo Wang, Yaqian Li, Kaiwen Long, Ming Yang, Yiqing Shen
Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities
Mahdi Erfanian, H. V. Jagadish, Abolfazl Asudeh
VISION-MAE: A Foundation Model for Medical Image Segmentation and Classification
Zelong Liu, Andrew Tieu, Nikhil Patel, Alexander Zhou, George Soultanidis, Zahi A. Fayad, Timothy Deyer, Xueyan Mei
InfMAE: A Foundation Model in the Infrared Modality
Fangcen Liu, Chenqiang Gao, Yaming Zhang, Junjie Guo, Jinhao Wang, Deyu Meng
Weaver: Foundation Models for Creative Writing
Tiannan Wang, Jiamin Chen, Qingrui Jia, Shuai Wang, Ruoyu Fang, Huilin Wang, Zhaowei Gao, Chunzhao Xie, Chuou Xu, Jihong Dai, Yibin Liu, Jialong Wu, Shengwei Ding, Long Li, Zhiwei Huang, Xinle Deng, Teng Yu, Gangan Ma, Han Xiao, Zixin Chen, Danjun Xiang, Yunxia Wang, Yuanyuan Zhu, Yi Xiao, Jing Wang, Yiru Wang, Siran Ding, Jiayang Huang, Jiayi Xu, Yilihamu Tayier, Zhenyu Hu, Yuan Gao, Chengfeng Zheng, Yueshu Ye, Yihang Li, Lei Wan, Xinyue Jiang, Yujie Wang, Siyu Cheng, Zhule Song, Xiangru Tang, Xiaohua Xu, Ningyu Zhang, Huajun Chen, Yuchen Eleanor Jiang, Wangchunshu Zhou
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash, Nikhil Muralidhar, Walid Saad, Naren Ramakrishnan