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
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction—0
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies—0
Hierarchical Poset Decoding for Compositional Generalization in Language—0
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three—0
Structural Knowledge Distillation: Tractably Distilling Information for Structured PredictorCode0
An Empirical Investigation of Beam-Aware Training in SupertaggingCode0
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks—0
Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction—0
Towards Structured Prediction in Bioinformatics with Deep Learning—0
DiverseNet: When One Right Answer is not Enough—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified