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 - Page 39
ABA Learning via ASP
Emanuele De Angelis, Maurizio Proietti, Francesca ToniDomain Generalization without Excess Empirical Risk
Ozan Sener, Vladlen KoltunBenchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
Yangkun Chen, Joseph Suarez, Junjie Zhang, Chenghui Yu, Bo Wu, Hanmo Chen, Hengman Zhu, Rui Du, Shanliang Qian, Shuai Liu, Weijun Hong+10
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Franziska Braun, Sebastian P. Bayerl, Paula A. Pérez-Toro, Florian Hönig, Hartmut Lehfeld, Thomas Hillemacher, Elmar Nöth, Tobias Bocklet+1It Ain't That Bad: Understanding the Mysterious Performance Drop in OOD Generalization for Generative Transformer Models
Xingcheng Xu, Zihao Pan, Haipeng Zhang, Yanqing Yang