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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 639 papers

TitleStatusHype
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Nested Named Entity Recognition as Single-Pass Sequence Labeling—0
Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples—0
Structured Prediction with Abstention via the Lovász Hinge—0
Predicting Through Generation: Why Generation Is Better for Prediction—0
Volume Optimality in Conformal Prediction with Structured Prediction Sets—0
A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization—0
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal LearningCode1
Learning Differentiable Surrogate Losses for Structured Prediction—0
Meaning Typed Prompting: A Technique for Efficient, Reliable Structured Output GenerationCode1
Conformal Structured PredictionCode0
Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing—0
Sigma Flows for Image and Data Labeling and Learning Structured Prediction—0
Structured Prediction in Online Learning—0
Deep Sketched Output Kernel Regression for Structured PredictionCode0
To be Continuous, or to be Discrete, Those are Bits of QuestionsCode0
CF-OPT: Counterfactual Explanations for Structured PredictionCode0
HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis—0
Beyond MLE: Investigating SEARNN for Low-Resourced Neural Machine Translation—0
Semantic Loss Functions for Neuro-Symbolic Structured Prediction—0
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction—0
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode—0
Structured Language Generation Model for Robust Structure Prediction—0
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss—0
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified