Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510

This article summarizes a podcast episode featuring José Miguel Hernández-Lobato, a machine learning lecturer. The discussion centers on his work combining Bayesian learning and deep learning, specifically in molecular design and discovery. The episode explores his application of these methods to identify chemical reactions, both in 2D and 3D spaces. Key challenges addressed include sample efficiency and objective function creation. The conversation also touches upon integrating the Bayesian approach into Reinforcement Learning (RL) problems and highlights other relevant research papers. The article provides a concise overview of the episode's key topics and research areas.

• •The episode explores the intersection of Bayesian learning and deep learning.
• •The focus is on applying these methods to molecular design and discovery.
• •Challenges like sample efficiency and objective function creation are discussed.