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 181–190 of 639 papers

TitleStatusHype
Efficient multiple hyperparameter learning for log-linear models—0
Efficient Multi-Template Learning for Structured Prediction—0
Efficient non-greedy optimization of decision trees—0
Beyond MLE: Investigating SEARNN for Low-Resourced Neural Machine Translation—0
Efficient Structured Surrogate Loss and Regularization in Structured Prediction—0
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces—0
Book Review: Linguistic Structure Prediction by Noah A. Smith—0
A Study of Latent Structured Prediction Approaches to Passage Reranking—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified