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 261–270 of 639 papers

TitleStatusHype
Multi-agent query reformulation: Challenges and the role of diversity—0
Structured Knowledge Distillation for Dense PredictionCode0
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction—0
Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing—0
Scaling Matters in Deep Structured-Prediction Models—0
A Smoother Way to Train Structured Prediction ModelsCode0
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling—0
A General Theory for Structured Prediction with Smooth Convex Surrogates—0
Learning Hierarchical Interactions at Scale: A Convex Optimization ApproachCode0
Extracting Multiple-Relations in One-Pass with Pre-Trained TransformersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified