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
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling—0
A General Theory for Structured Prediction with Smooth Convex Surrogates—0
Learning Hierarchical Interactions at Scale: A Convex Optimization ApproachCode0
Extracting Multiple-Relations in One-Pass with Pre-Trained TransformersCode0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Learning with Fenchel-Young LossesCode0
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks—0
Compact Representation of Uncertainty in Clustering—0
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses—0
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified