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 231–240 of 639 papers

TitleStatusHype
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker—0
Generalization Analysis on Learning with a Concurrent Verifier—0
Fairness constraints can help exact inference in structured prediction—0
Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning—0
Canonical Correlation Inference for Mapping Abstract Scenes to Text—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Global Model for Hierarchical Multi-Label Text Classification—0
Gradient-based Inference for Networks with Output Constraints—0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)—0
Candidate Constrained CRFs for Loss-Aware Structured Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified