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
Provable In-Context Learning of Linear Systems and Linear Elliptic PDEs with Transformers
Frank Cole, Yulong Lu, Riley O'Neill, Tianhao Zhang
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
Vasilii Feofanov, Romain Ilbert, Malik Tiomoko, Themis Palpanas, Ievgen Redko
Human-like Affective Cognition in Foundation Models
Kanishk Gandhi, Zoe Lynch, Jan-Philipp Fränken, Kayla Patterson, Sharon Wambu, Tobias Gerstenberg, Desmond C. Ong, Noah D. Goodman
Estimating Wage Disparities Using Foundation Models
Keyon Vafa, Susan Athey, David M. Blei
Veridical Data Science for Medical Foundation Models
Ahmed Alaa, Bin Yu
A Survey of Foundation Models for Music Understanding
Wenjun Li, Ying Cai, Ziyang Wu, Wenyi Zhang, Yifan Chen, Rundong Qi, Mengqi Dong, Peigen Chen, Xiao Dong, Fenghao Shi, Lei Guo, Junwei Han, Bao Ge, Tianming Liu, Lin Gan, Tuo Zhang
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare
Chia-Hao Li, Niraj K. Jha
On the Generalizability of Foundation Models for Crop Type Mapping
Yi-Chia Chang, Adam J. Stewart, Favyen Bastani, Piper Wolters, Shreya Kannan, George R. Huber, Jingtong Wang, Arindam Banerjee
Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval
Amirreza Mahbod, Nematollah Saeidi, Sepideh Hatamikia, Ramona Woitek
Prevailing Research Areas for Music AI in the Era of Foundation Models
Megan Wei, Mateusz Modrzejewski, Aswin Sivaraman, Dorien Herremans
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks
S. Kawa Atapour, S. Jamal SeyedMohammadi, S. Mohammad Sheikholeslami, Jamshid Abouei, Konstantinos N. Plataniotis, Arash Mohammadi
Eureka: Evaluating and Understanding Large Foundation Models
Vidhisha Balachandran, Jingya Chen, Neel Joshi, Besmira Nushi, Hamid Palangi, Eduardo Salinas, Vibhav Vineet, James Woffinden-Luey, Safoora Yousefi
Affective Computing Has Changed: The Foundation Model Disruption
Björn Schuller, Adria Mallol-Ragolta, Alejandro Peña Almansa, Iosif Tsangko, Mostafa M. Amin, Anastasia Semertzidou, Lukas Christ, Shahin Amiriparian
Uncertainty and Generalizability in Foundation Models for Earth Observation
Raul Ramos-Pollan, Freddie Kalaitzis, Karthick Panner Selvam
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Danli Shi, Weiyi Zhang, Jiancheng Yang, Siyu Huang, Xiaolan Chen, Mayinuer Yusufu, Kai Jin, Shan Lin, Shunming Liu, Qing Zhang, Mingguang He
High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study
Shijie Chang, Lihe Zhang, Huchuan Lu