L2hforadaptivity ✰

This acts as a powerful regularizer. It prevents the model from becoming over-confident in its errors, making the decision boundary smoother and more robust to noise.

In the traditional paradigm of supervised learning, we teach machines to be confident. We show a model an image of a cat, and we demand it output [Cat: 1.0, Dog: 0.0] . This is the world of —a binary world of right and wrong. l2hforadaptivity

In this post, we’ll dive into what L2H is, why Hard Labels fail adaptivity, and how "softening" our targets leads to smarter AI. This acts as a powerful regularizer