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• W2999688250 abstract "Structured prediction requires manipulating a large number of combinatorial structures, e.g., dependency trees or alignments, either as latent or output variables. Recently, the SparseMAP method has been proposed as a differentiable, sparse alternative to maximum a posteriori (MAP) and marginal inference. SparseMAP returns a combination of a small number of structures, a desirable property in some downstream applications. However, SparseMAP requires a tractable MAP inference oracle. This excludes, e.g., loopy graphical models or factor graphs with logic constraints, which generally require approximate inference. In this paper, we introduce LP-SparseMAP, an extension of SparseMAP that addresses this limitation via a local polytope relaxation. LP-SparseMAP uses the flexible and powerful domain specific language of factor graphs for defining and backpropagating through arbitrary hidden structure, supporting coarse decompositions, hard logic constraints, and higher-order correlations. We derive the forward and backward algorithms needed for using LP-SparseMAP as a hidden or output layer. Experiments in three structured prediction tasks show benefits compared to SparseMAP and Structured SVM." @default.
• W2999688250 created "2020-01-23" @default.
• W2999688250 creator A5051693368 @default.
• W2999688250 creator A5064085103 @default.
• W2999688250 date "2020-01-13" @default.
• W2999688250 modified "2023-09-27" @default.
• W2999688250 title "LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction" @default.
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