Discrete Flow Matching for High-Dimensional Discrete Data Generation
Offered By: Valence Labs via YouTube
Course Description
Overview
Explore a comprehensive lecture on Discrete Flow Matching, a novel discrete flow paradigm for generating high-dimensional discrete data. Delve into the key contributions of this approach, including its use of general probability paths, generic sampling formula, and improved generative perplexity. Learn how Discrete Flow Matching scales up to 1.7B parameters, achieving significant results on coding benchmarks. Understand the non-autoregressive generation of high-quality discrete data and how it narrows the gap between autoregressive models and discrete flow models. Follow the detailed equation walkthrough, examine the results, and engage with the Q&A session to deepen your understanding of this innovative technique in AI for drug discovery.
Syllabus
- Intro + Background
- Equation Walkthrough
- Results
- Q+A
Taught by
Valence Labs
Related Courses
Diffusion Models Beat GANs on Image Synthesis - Machine Learning Research Paper ExplainedYannic Kilcher via YouTube Diffusion Models Beat GANs on Image Synthesis - ML Coding Series - Part 2
Aleksa Gordić - The AI Epiphany via YouTube OpenAI GLIDE - Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Aleksa Gordić - The AI Epiphany via YouTube Food for Diffusion
HuggingFace via YouTube Imagen: Text-to-Image Generation Using Diffusion Models - Lecture 9
University of Central Florida via YouTube