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 301–310 of 639 papers

TitleStatusHype
"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction—0
Learning Differentiable Surrogate Losses for Structured Prediction—0
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training—0
Learning Distributed Representations for Structured Output Prediction—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP—0
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables—0
Learning Ensembles of Structured Prediction Rules—0
Dialogue Act Recognition via CRF-Attentive Structured Network—0
Improved CCG Parsing with Semi-supervised Supertagging—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified