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On the Fundamental Limits of Exact Inference in Structured Prediction
[article]
2021
arXiv
pre-print
Inference is a main task in structured prediction and it is naturally modeled with a graph. In the context of Markov random fields, noisy observations corresponding to nodes and edges are usually involved, and the goal of exact inference is to recover the unknown true label for each node precisely. The focus of this paper is on the fundamental limits of exact recovery irrespective of computational efficiency, assuming the generative process proposed by Globerson et al. (2015). We derive the
arXiv:2102.08895v1
fatcat:xktm764ip5e4nlhgzsc4nhejsa