Explore the cutting-edge world of generative modeling and inference in this 44-minute lecture by Arnaud Doucet from Oxford University. Delve into the powerful class of denoising diffusion models, understanding their capabilities and limitations. Discover how recent alternative approaches based on transport ideas can overcome these constraints. Focus on the diffusion Schrodinger bridge, an entropy-regularized version of optimal transport, and learn about its numerical approximation methods. Gain insights into the evolving landscape of generative modeling techniques and their applications in the field of artificial intelligence and machine learning.
From Denoising Diffusion Models to Transport for Generative Modeling and Inference
Alan Turing Institute via YouTube
Overview
Syllabus
From denoising diffusion models to transport for generative modelling and inference
Taught by
Alan Turing Institute
Related Courses
-
Convergence of Denoising Diffusion Models Under the Manifold Hypothesis
-
Deep Generative Models and Stable Diffusion: Revolution in Visual Synthesis - Lecture by Björn Ommer
-
Neural SDEs - Deep Generative Models in the Diffusion Limit - Maxim Raginsky
-
Stanford CS236: Deep Generative Models - Score Based Diffusion Models - Lecture 16
-
Denoising Diffusion-Based Generative Modeling
-
Deep Generative Modeling - MIT 6.S191 Lecture 4
