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 76–100 of 639 papers

TitleStatusHype
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
Promptly Predicting Structures: The Return of InferenceCode0
Lazy-k: Decoding for Constrained Token ClassificationCode0
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
A Unified View of Evaluation Metrics for Structured PredictionCode0
"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction—0
Contextual Label Projection for Cross-Lingual Structured PredictionCode0
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
On Regularization and Inference with Label Constraints—0
Training Multimedia Event Extraction With Generated Images and Captions—0
On Certified Generalization in Structured Prediction—0
Partial Inference in Structured Prediction—0
Computing a partition function of a generalized pattern-based energy over a semiring—0
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective—0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
Modified Gauss-Newton Algorithms under Noise—0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with KernelsCode0
PAC Prediction Sets for Large Language Models of CodeCode0
Diverse Probabilistic Trajectory Forecasting with Admissibility ConstraintsCode0
Exact Inference in High-order Structured Prediction—0
Backpropagation of Unrolled Solvers with Folded Optimization—0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified