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 401–425 of 639 papers

TitleStatusHype
Learning Kernels for Structured Prediction using Polynomial Kernel Transformations—0
Learning latent variable structured prediction models with Gaussian perturbations—0
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time—0
Learning Max-Margin Tree Predictors—0
Learning Mixtures of Submodular Functions for Image Collection Summarization—0
Learning Multi-target Tracking with Quadratic Object Interactions—0
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions—0
Learning Output Embeddings in Structured Prediction—0
Learning proposals for sequential importance samplers using reinforced variational inference—0
Learning Rich Representations For Structured Visual Prediction Tasks—0
Learning Stochastic Feedforward Neural Networks—0
Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features—0
Learning Structured Predictors from Bandit Feedback for Interactive NLP—0
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective—0
Learning to generalize to new compositions in image understanding—0
Learning to Jointly Predict Ellipsis and Comparison Structures—0
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing—0
Learning to Search Better Than Your Teacher—0
Learning to Search in Branch and Bound Algorithms—0
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects—0
Learning to Stop in Structured Prediction for Neural Machine Translation—0
Learning to Summarise Related Sentences—0
Learning with Differentiable Pertubed Optimizers—0
Learning with Limited Text Data—0
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified