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 161–170 of 639 papers

TitleStatusHype
Discriminative Metric Learning by Neighborhood Gerrymandering—0
Discriminative Segmental Cascades for Feature-Rich Phone Recognition—0
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction—0
Distributed Training of Structured SVM—0
A Study of State Aliasing in Structured Prediction with RNNs—0
DiverseNet: When One Right Answer is not Enough—0
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses—0
Document Context Neural Machine Translation with Memory Networks—0
A Study of Latent Structured Prediction Approaches to Passage Reranking—0
A Learning Error Analysis for Structured Prediction with Approximate Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified