#ml-theory News & Analysis

Quantifying and Optimizing Simplicity via Polynomial Representations

Researchers introduce polynomial representations as a quantitative measure of neural network simplicity, demonstrating that the effective degree of these representations predicts generalization better than existing metrics. The approach yields a differentiable regularizer that improves performance across image classification, text tasks, vision-language models, and reinforcement learning.