In this intriguing paper, the concept of “grokking” in machine learning is explored. Grokking involves delayed generalization, occurring after extreme overfitting to training data. The author aims to speed up the generalization process by analyzing the trajectory of parameter gradients during training iterations, isolating components responsible for overfitting and generalization. By focusing on enhancing the slow-varying components, the grokking phenomenon can be accelerated significantly, allowing for rapid application across various tasks like image recognition, language processing, and graph analysis. This innovative approach showcases the author’s ability to tackle complex machine learning challenges with just a few lines of code.
https://arxiv.org/abs/2405.20233