The Local Inconsistency Resolution Algorithm
Oliver Richardson
Keywords:
Learning
Approximate Inference
conrol
attention
message passing
probabilistic dependency graphs
gradient flow
Abstract
We present a generic algorithm for learning and approximate inference across a broad class of statistical models, that unifies many approaches in the literature. Our algorithm, called local inconsistency resolution (LIR), has an intuitive epistemic interpretation. It is based on the theory of probabilistic dependency graphs (PDGs), an expressive class of graphical models rooted in information theory, which can capture inconsistent beliefs.
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