Mathematical Foundations

Mathematical Foundations for Generative AI

Building rigorous theoretical frameworks for understanding and improving generative models.

Mean-Field Games

Understanding generative modeling through the lens of game theory and optimal transport.

Wasserstein Proximals

Geometric perspectives on score-based models and their convergence properties.

PDE Analysis

Continuous-time formulations providing convergence guarantees and theoretical insights.

Neural Architectures

Optimal control perspectives for understanding transformer architectures and attention mechanisms.

More content coming soon…