2026
A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
ICML 2026poster
Neural networks trained with gradient-based methods exhibit a strong simplicity bias, learning simpler statistical features of their data before moving to more complex features. In this work, we study this bias from a Fourier perspective, motivated by the approximate translation-invariance and the c…