AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Prior information for real-world physical quantities is most elegantly expressed via partial differential equations (PDEs). In this paper, we propose a novel way to solve PDEs using probabilistic inference on a factor graph. In general, factor graphs provide a natural way to encode prior knowledge into a model as expli...
Direct Message Approximation (DMA), which approximates factor-to-variable messages directly rather than the marginal, is introduced and a master theorem (proper messages, any graph) bounding marginal KL from message KL is proved, with three structural corollaries.
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