Go deeper: https://mltheory.org/deep.pdf

The "double descent" phenomenon in machine learning challenges traditional understandings of the bias-variance tradeoff. Double descent describes a pattern where, beyond a certain model complexity, test error decreases again after an initial rise. This occurs not only with increasing model size, but also with training time and, surprisingly, dataset size. The concept of "effective model complexity" suggests that atypical behavior arises when this complexity is comparable to the number of training samples. The collective findings suggest that larger models and longer training times can sometimes improve performance, even after initial overfitting. These insights have implications for understanding the generalization capabilities of modern deep learning models.

Hosted on Acast. See acast.com/privacy for more information.