
Dive into the fundamentals of diffusion models with MIT Prof. Song Han, exploring their architecture, principles, and applications in machine learning and AI generation.

Dive into the fundamentals of diffusion models, exploring their architecture, principles, and applications in machine learning through expert guidance from MIT's Professor Song Han.

Explore groundbreaking techniques for training diffusion models with corrupted data, using Tweedie's formula to generate high-quality samples while reducing memorization and privacy concerns.

Explore how dp-promise leverages diffusion model noise to achieve differential privacy in image synthesis while maintaining high image quality and protecting sensitive training data.