Explore the revolutionary world of diffusion models, a cutting-edge AI technology that learns to reverse the process of turning data into noise to generate new, high-quality content.

We'll break down the science behind these models, including how they use stochastic differential equations (SDEs) to transform data and the role of the score function in guiding the reverse process. We'll discuss how methods like SMLD and DDPM fit into this framework, and examine the differences between VE and VP SDEs, and how they relate to different types of noise.

We'll cover sampling methods like predictor-corrector (PC) samplers, and how they combine prediction and correction for better results. You'll also learn about the many applications of diffusion models, including image and music generation, protein design, text-to-image synthesis, controllable text generation and solving inverse problems.

We'll touch on conditional generation using techniques like classifier guidance and classifier-free guidance, and how they allow for more control and adaptability.

Finally, we'll explore how diffusion models are being used for black-box optimization, and why the quality of training data matters.

Online Tutorials:

• "Understanding Diffusion Models: A Deep Dive into Generative AI" on Unite.AI: An in-depth article exploring the workings of diffusion models and their significance in generative AI.

  
  

• "Diffusion and Score-Based Generative Models" on MIT OpenCourseWare: A tutorial covering the theory, methods, and applications of diffusion and score-based generative models.

Whether you're an AI enthusiast, researcher, or curious listener, this episode will ignite your imagination and inspire you to dream big.

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