Strong Generalization
Strong generalization, the ability of machine learning models to perform well on unseen data, is a central objective in current research. Active areas of investigation include improving the robustness of self-supervised learning, understanding the optimization dynamics of transformers and other architectures (including CNNs and RNNs), and developing methods to enhance generalization through data augmentation, regularization techniques (e.g., logical regularization, consistency regularization), and improved training strategies (e.g., few-shot learning, meta-learning). These advancements are crucial for building reliable and adaptable AI systems across diverse applications, from image classification and natural language processing to healthcare and robotics.
Papers
Generalization and Estimation Error Bounds for Model-based Neural Networks
Avner Shultzman, Eyar Azar, Miguel R. D. Rodrigues, Yonina C. Eldar
AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNs
Shengrui Li, Xueting Han, Jing Bai
Information Geometrically Generalized Covariate Shift Adaptation
Masanari Kimura, Hideitsu Hino
Investigating the Nature of 3D Generalization in Deep Neural Networks
Shoaib Ahmed Siddiqui, David Krueger, Thomas Breuel
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation
Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang, Yu Wang, Lechao Cheng, Jie Song, Mingli Song
Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning
Brad Windsor, Brandon O'Shea, Mengxi Wu
Learning and generalization of compositional representations of visual scenes
E. Paxon Frady, Spencer Kent, Quinn Tran, Pentti Kanerva, Bruno A. Olshausen, Friedrich T. Sommer
Generalization with data-dependent quantum geometry
Tobias Haug, M. S. Kim
Improving Generalization with Domain Convex Game
Fangrui Lv, Jian Liang, Shuang Li, Jinming Zhang, Di Liu