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 541–550 of 639 papers

TitleStatusHype
Learning to Summarise Related Sentences—0
Priberam: A Turbo Semantic Parser with Second Order Features—0
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators—0
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions—0
Reinforcement and Imitation Learning via Interactive No-Regret Learning—0
Techniques for Learning Binary Stochastic Feedforward Neural Networks—0
Syntactic Processing Using Global Discriminative Learning and Beam-Search Decoding—0
Zero-shot Entity Extraction from Web Pages—0
Representation Learning for Text-level Discourse ParsingCode0
A Discriminative Graph-Based Parser for the Abstract Meaning Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified