Scalable Inference of Dynamic Graphical Models with Combinatorial Structures

Scalable Inference of Dynamic Graphical Models with Combinatorial Structures

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a dynamic graphical models lecture focusing on scalable inference techniques for structures with combinatorial properties. Delve into advanced concepts presented by Salar Fattahi, Assistant Professor of Industrial and Operations Engineering at the University of Michigan. Gain insights into cutting-edge research and methodologies in this 30-minute talk, which offers valuable knowledge for those interested in graphical models, combinatorial optimization, and scalable inference algorithms.