One-Shot Marginal MAP Inference in Markov Random Fields
We propose a novel variational inference strategy that is flexible enough to handle both
continuous and discrete random variables, efficient enough to be able to handle repeated
statistical inferences, and scalable enough, via modern GPUs, to be practical on MRFs with
hundreds of thousands of random variables. We prove that our approach overcomes weaknesses
of the current approaches and demonstrate the efficacy of our approach on both synthetic models and
real-world applications.
Hao Xiong, Yuanzhen Guo *, Yibo Yang
*, Nicholas Ruozzi
UAI 2019