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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 541–550 of 639 papers

TitleStatusHype
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle—0
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
Reinforcement and Imitation Learning via Interactive No-Regret Learning—0
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions—0
Techniques for Learning Binary Stochastic Feedforward Neural Networks—0
Zero-shot Entity Extraction from Web Pages—0
Representation Learning for Text-level Discourse ParsingCode0
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction—0
Show:102550
← PrevPage 55 of 64Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified