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 171–180 of 639 papers

TitleStatusHype
Energy Disaggregation with Semi-supervised Sparse Coding—0
Bayesian Kernel Methods for Natural Language Processing—0
Dual Coordinate Descent Algorithms for Efficient Large Margin Structured Prediction—0
Dynamic Feature Selection for Dependency Parsing—0
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation—0
A Study of State Aliasing in Structured Prediction with RNNs—0
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis—0
Efficient and Consistent Adversarial Bipartite Matching—0
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies—0
A Study of Latent Structured Prediction Approaches to Passage Reranking—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified