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 331–340 of 639 papers

TitleStatusHype
Classical Structured Prediction Losses for Sequence to Sequence Learning—0
Document Context Neural Machine Translation with Memory Networks—0
Optimality of Approximate Inference Algorithms on Stable Instances—0
Deep Learning in Lexical Analysis and Parsing—0
Dense and Low-Rank Gaussian CRFs Using Deep EmbeddingsCode0
Active Learning amidst Logical KnowledgeCode0
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic MapsCode0
Optimizing for Measure of Performance in Max-Margin Parsing—0
A Unified Framework for Structured Prediction: From Theory to Practice—0
Structured Prediction via Learning to Search under Bandit Feedback—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified