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 91–100 of 639 papers

TitleStatusHype
Predictive Inference with Weak Supervision—0
Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedbackCode1
Interscript: A dataset for interactive learning of scripts through error feedbackCode1
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
Towards Sharper Generalization Bounds for Structured Prediction—0
Safe Screening for Sparse Conditional Random Fields—0
Multi-fidelity Stability for Graph Representation Learning—0
FastDOG: Fast Discrete Optimization on GPUCode1
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis—0
SegMix: A Simple Structure-Aware Data Augmentation Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified