Linear Separability

Linear separability, the ability to perfectly classify data points using a single hyperplane, is a fundamental concept in machine learning, with research focusing on understanding its relationship to model generalization and performance. Current investigations explore how near-separability affects model training dynamics (e.g., "grokking"), how to leverage separability for efficient data pruning and feature selection, and how to enhance separability within various architectures, including neural ordinary differential equations, graph neural networks, and transformers. These studies are crucial for improving model efficiency, generalization, and robustness across diverse applications, from image classification to global optimization and continual learning.