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 281–290 of 639 papers

TitleStatusHype
Joint Event Extraction via Structured Prediction with Global Features—0
Deep imitation learning for molecular inverse problems—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
Deeply Learning the Messages in Message Passing Inference—0
Language Classification and Segmentation of Noisy Documents in Hebrew Scripts—0
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction—0
Large-Margin Learning of Submodular Summarization Models—0
Large Margin Semi-supervised Structured Output Learning—0
Improving Joint Training of Inference Networks and Structured Prediction Energy Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified