Modular Manifolds: constraining weight geometry to stabilize training
- Source
- x.com
- Date
Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices. https://t.co/PGG4zy3u23 We explore a fundamental understanding of the geometry of neural network optimization.
Context
Training becomes harder to control when a neural network's internal weights grow or shrink unpredictably. Thinking Machines' Modular Manifolds proposal constrains those weights to specified mathematical shapes and designs the update rule around those constraints. This makes the size and effect of a training step easier to reason about. The article introduces the approach.
The modular part addresses how layers interact: it adjusts each layer's learning rate according to its position and influence in the combined network. The contribution is a framework for designing more controlled training algorithms. A small image-classification experiment supports further investigation, while efficient implementation at large scale remains an explicit research question. Method and future work.
Checking sign-in…
Loading comments…