Model Generalizability
Model generalizability, the ability of a machine learning model to perform well on unseen data or tasks, is a critical area of research aiming to improve the robustness and reliability of AI systems. Current efforts focus on enhancing generalizability through techniques like meta-learning, data augmentation strategies (including adversarial methods), and architectural innovations such as multi-task learning and the incorporation of prior knowledge or numerical priors into model design. Improved generalizability is crucial for deploying AI models in real-world applications across diverse and unpredictable conditions, impacting fields ranging from healthcare and robotics to materials science and network security.
34papers
Papers
January 2, 2025
November 5, 2024
October 11, 2024
July 22, 2024
Efficient and generalizable prediction of molecular alterations in multiple cancer cohorts using H&E whole slide images
Kshitij Ingale, Sun Hae Hong, Qiyuan Hu, Renyu Zhang, Bo Osinski, Mina Khoshdeli, Josh Och, Kunal Nagpal, Martin C. Stumpe, Rohan P. JoshiLearning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning
Zhecheng Yuan, Tianming Wei, Shuiqi Cheng, Gu Zhang, Yuanpei Chen, Huazhe Xu
March 18, 2024