Graph partition strategies for generalized mean field inference. Eric P. Xing. Computer Science Division. University of California. Berkeley, CA 94720. Michael ...
Jul 11, 2012 · Title:Graph partition strategies for generalized mean field inference. Authors:Eric P. Xing, Michael I. Jordan, Stuart Russell. Download a PDF ...
This paper presents a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm that optimizes over ...
In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. This combination ...
In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. This combination ...
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In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. This combination ...
Bibliographic details on Graph Partition Strategies for Generalized Mean Field Inference.
Our learning based approach is also capable of generalization, meaning that we can train a model ... partitions at inference ... Mean-field theory of graph neural.
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Mean field theory (MRT), also known as variational methods, offers a strategy to design inference algorithms for MRF models. The approach has several advantages ...