This episode discussed a research paper exploring neural network generalization on tiny, algorithmically generated datasets. The authors found a phenomenon called "grokking" where validation accuracy suddenly jumps after apparent overfitting. This suggests deeper understanding of patterns beyond memorization. The study revealed that grokking is tied to dataset size and optimization, and that weight decay helps. The use of binary operation tables with abstract symbols eliminates inherent structure and focuses on symbol relationships. Double descent in validation loss, where it initially decreases, increases (overfitting), and then decreases again, was observed, suggesting a link between optimization landscape and generalization. The authors also visualized learned embeddings, revealing patterns reflecting the underlying mathematical operations. The research challenges our understanding of deep learning generalization by highlighting the crucial role of optimization and the emergence of generalization even after overfitting. It opens new avenues for research into the fundamental mechanisms of deep learning.
Original paper: [ Ссылка ]
