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 26–50 of 639 papers

TitleStatusHype
RxnScribe: A Sequence Generation Model for Reaction Diagram ParsingCode1
Autoregressive Structured Prediction with Language ModelsCode1
StructPool: Structured Graph Pooling via Conditional Random FieldsCode1
Structured Output Learning with Conditional Generative FlowsCode1
Structured Prediction as Translation between Augmented Natural LanguagesCode1
Code4Struct: Code Generation for Few-Shot Event Structure PredictionCode1
Assignment-Space-Based Multi-Object Tracking and SegmentationCode1
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online LearningCode1
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer NetworksCode1
Adversarial Attack and Defense of Structured Prediction ModelsCode1
CrossBeam: Learning to Search in Bottom-Up Program SynthesisCode1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
Fast and Accurate Entity Recognition with Iterated Dilated ConvolutionsCode1
FastDOG: Fast Discrete Optimization on GPUCode1
Group-Wise Semantic Mining for Weakly Supervised Semantic SegmentationCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
Active Imitation Learning with Noisy GuidanceCode1
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement LearningCode1
Learning Approximate Inference Networks for Structured PredictionCode1
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal LearningCode1
Automated Concatenation of Embeddings for Structured PredictionCode1
A Frustratingly Easy Approach for Entity and Relation ExtractionCode1
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
DEGREE: A Data-Efficient Generation-Based Event Extraction ModelCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified