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
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale TasksCode1
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
CrossBeam: Learning to Search in Bottom-Up Program SynthesisCode1
Structured Output Learning with Conditional Generative FlowsCode1
Structured Prediction as Translation between Augmented Natural LanguagesCode1
Energy-Based Learning for Scene Graph GenerationCode1
Deep Structured Prediction for Facial Landmark DetectionCode1
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online LearningCode1
Assignment-Space-Based Multi-Object Tracking and SegmentationCode1
Adversarial Attack and Defense of Structured Prediction ModelsCode1
DEGREE: A Data-Efficient Generation-Based Event Extraction ModelCode1
Automated Concatenation of Embeddings for Structured PredictionCode1
FastDOG: Fast Discrete Optimization on GPUCode1
Autoregressive Structured Prediction with Language ModelsCode1
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer NetworksCode1
Instance-Based Learning of Span Representations: A Case Study through Named Entity RecognitionCode1
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement LearningCode1
Active Imitation Learning with Noisy GuidanceCode1
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal LearningCode1
Code4Struct: Code Generation for Few-Shot Event Structure PredictionCode1
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured PredictionCode1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
A Frustratingly Easy Approach for Entity and Relation ExtractionCode1
Estimating Gradients for Discrete Random Variables by Sampling without ReplacementCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified