Explore a 51-minute conference talk on Local Search GFlowNets presented by Minsu Kim from Valence Labs. Dive into the innovative training algorithm designed to enhance GFlowNets' sampling quality through local search methods. Learn how this approach improves the effectiveness of GFlowNets in various discovery tasks, particularly in drug discovery. Understand the challenges of vast combinatorial spaces and how local search techniques, including backtracking and trajectory reconstruction, target high-reward regions more efficiently. Examine the algorithm's structure, experimental results, and participate in a Q&A session. Gain insights into the potential applications of this advanced AI technique in the field of drug discovery and beyond.
Overview
Syllabus
- Intro + Background
- LS-GFN Algorithm
- Experiments
- Q&A
Taught by
Valence Labs
Related Courses
-
Improvements on GFlowNets Applied to Molecular Discovery
-
Evaluating Retrosynthesis with Syntheseus and Retro-Fallback
-
Diffusion Models for Sampling Rare Events in Chemistry
-
Exploring Molecular Space and Active Learning in Drug Discovery
-
Applying Active Learning in Drug Discovery - There's No Free Lunch, but You Can Get a Discount
-
Protein Discovery with Discrete Walk-Jump Sampling
