Muon Dynamics as a Spectral Wasserstein Flow

1. 1Formulate Muon Dynamics as a Spectral Wasserstein Flow
2. 2Apply spectral normalization rules to stabilize deep learning optimization
3. 3Analyze the effect of spectral normalization on deep architectures
4. 4Compare spectral normalization with coordinatewise Euclidean normalization

This research benefits machine learning researchers and engineers working on deep learning optimization, as it provides a new perspective on gradient normalization and spectral normalization rules.

💡 Spectral normalization rules can be more faithful than coordinatewise Euclidean ones for deep architectures

💡 Muon Dynamics as a Spectral Wasserstein Flow stabilizes deep learning optimization