Markov Chain Monte Carlo
Tim explains the Markov property of Markov chain Monte Carlo, emphasizing the sequential process of generating random samples. He delves into the challenges of capturing distributions in high dimensions and the potential of machine learning to provide a more efficient solution. The discussion touches on systematic generalization and the concept of GFLO nets in active learning frameworks.In this clip
From this podcast
Machine Learning Street Talk (MLST)
#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality
Related Questions