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 126–150 of 639 papers

TitleStatusHype
Measuring and Reducing Model Update Regression in Structured Prediction for NLP—0
Structured Prediction Problem ArchiveCode0
Constrained Structure Learning for Scene Graph Generation—0
Neuro-Symbolic Entropy Regularization—0
Predictive Inference with Weak Supervision—0
Towards Sharper Generalization Bounds for Structured Prediction—0
Safe Screening for Sparse Conditional Random Fields—0
Multi-fidelity Stability for Graph Representation Learning—0
SegMix: A Simple Structure-Aware Data Augmentation Method—0
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis—0
Word Reordering for Zero-shot Cross-lingual Structured Prediction—0
Language Modelling via Learning to Rank—0
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source DataCode0
Structured Energy Network as a dynamic loss function. Case study. A case study with multi-label Classification—0
Multi-task Learning with Domain Knowledge for Molecular Property Prediction—0
Structured Prediction in NLP -- A survey—0
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP—0
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision—0
Generic Oracles for Structured Prediction—0
Meta-Learning for Few-Shot Named Entity Recognition—0
Constraining Linear-chain CRFs to Regular LanguagesCode0
Identifying and Resolving Annotation Changes for Natural Language Understanding—0
Supervised Neural Clustering via Latent Structured Output Learning: Application to Question IntentsCode0
On the Consistency of Max-Margin Losses—0
Structured Convolutional Kernel Networks for Airline Crew SchedulingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified