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 581–590 of 639 papers

TitleStatusHype
A Constrained Latent Variable Model for Coreference Resolution—0
Online Learning for Inexact Hypergraph Search—0
An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views—0
Overcoming the Lack of Parallel Data in Sentence Compression—0
Feature Noising for Log-Linear Structured Prediction—0
Cross-Lingual Discriminative Learning of Sequence Models with Posterior Regularization—0
Learning Max-Margin Tree Predictors—0
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction—0
Sentence Compression with Joint Structural Inference—0
Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified