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 376–400 of 639 papers

TitleStatusHype
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Iterative Instance Segmentation—0
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction—0
Joint Event Extraction via Structured Prediction with Global Features—0
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit—0
Knowledge Tracing in Sequential Learning of Inflected Vocabulary—0
Language Classification and Segmentation of Noisy Documents in Hebrew Scripts—0
Language Modelling via Learning to Rank—0
Large-Margin Learning of Submodular Summarization Models—0
Large Margin Semi-supervised Structured Output Learning—0
Large scale evaluation of importance maps in automatic speech recognition—0
Latent Structured Active Learning—0
Latent Structures for Coreference Resolution—0
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations—0
Learning Adaptive Value of Information for Structured Prediction—0
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing—0
Learning Composable Energy Surrogates for PDE Order Reduction—0
Learning Differentiable Surrogate Losses for Structured Prediction—0
Learning Discriminators as Energy Networks in Adversarial Learning—0
Learning Distributed Representations for Structured Output Prediction—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP—0
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables—0
Learning Ensembles of Structured Prediction Rules—0
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified