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 341–350 of 639 papers

TitleStatusHype
Robust Conditional Probabilities—0
Robust Near-Isometric Matching via Structured Learning of Graphical Models—0
Robust Spoken Term Detection Automatically Adjusted for a Given Threshold—0
Safe Screening for Sparse Conditional Random Fields—0
Scalable Large-Margin Structured Learning: Theory and Algorithms—0
Scaling Matters in Deep Structured-Prediction Models—0
Search-Aware Tuning for Hierarchical Phrase-based Decoding—0
Search-based Structured Prediction—0
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks—0
Second-Order Constrained Parametric Proposals and Sequential Search-Based Structured Prediction for Semantic Segmentation in RGB-D Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified