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 101–110 of 639 papers

TitleStatusHype
An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views—0
Calibrating Structured Output Predictors for Natural Language Processing—0
A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing—0
Canonical Correlation Inference for Mapping Abstract Scenes to Text—0
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker—0
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings—0
Classical Structured Prediction Losses for Sequence to Sequence Learning—0
CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search—0
Closed-Form Training of Mahalanobis Distance for Supervised Clustering—0
Bayesian Kernel Methods for Natural Language Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified