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 24
Representations as Language: An Information-Theoretic Framework for Interpretability
On the Limitations of Fractal Dimension as a Measure of Generalization
DNCs Require More Planning Steps
Verifying the Generalization of Deep Learning to Out-of-Distribution Domains
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning