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 261–270 of 639 papers

TitleStatusHype
An Introduction to Conditional Random Fields—0
Entropy-Based Latent Structured Output Prediction—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three—0
Energy Disaggregation with Semi-supervised Sparse Coding—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
Energy Disaggregation via Discriminative Sparse Coding—0
Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking—0
End-to-End Neural Relation Extraction with Global Optimization—0
Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified