SOTAVerified

Structured Prediction

Structured Prediction is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search.

Source: Torch-Struct: Deep Structured Prediction Library

Papers

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Showing 401–410 of 639 papers

TitleStatusHype
Learning Kernels for Structured Prediction using Polynomial Kernel Transformations—0
Learning latent variable structured prediction models with Gaussian perturbations—0
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time—0
Learning Max-Margin Tree Predictors—0
Learning Mixtures of Submodular Functions for Image Collection Summarization—0
Learning Multi-target Tracking with Quadratic Object Interactions—0
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions—0
Learning Output Embeddings in Structured Prediction—0
Learning proposals for sequential importance samplers using reinforced variational inference—0
Learning Rich Representations For Structured Visual Prediction Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified