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 271–280 of 639 papers

TitleStatusHype
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
Distributionally Robust Graphical Models—0
Learning Beam Search Policies via Imitation LearningCode0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument TemplatesCode0
Show:102550
← PrevPage 28 of 64Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified