"foundation models" Papers
118 papers found • Page 3 of 3
Conference
One-Prompt to Segment All Medical Images
Wu, Min Xu
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
Lingyi Hong, Shilin Yan, Renrui Zhang et al.
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
Position: On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman et al.
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes, Michael Dennis, Jack Parker-Holder et al.
Position: Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, an liu, Zijun Liu et al.
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
Amrith Setlur, Saurabh Garg, Virginia Smith et al.
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
Yang Chen, Cong Fang, Zhouchen Lin et al.
Relational Programming with Foundational Models
Ziyang Li, Jiani Huang, Jason Liu et al.
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
Yufei Wang, Zhou Xian, Feng Chen et al.
Robustness Tokens: Towards Adversarial Robustness of Transformers
Brian Pulfer, Yury Belousov, Slava Voloshynovskiy
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil et al.
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Yunhe Gao
Transferring Knowledge From Large Foundation Models to Small Downstream Models
Shikai Qiu, Boran Han, Danielle Robinson et al.
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Shikun Feng, Yuyan Ni, Li et al.
V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Models
Heng Wang, Jianbo Ma, Santiago Pascual et al.
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma et al.