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
Structured Set Matching Networks for One-Shot Part Labeling—0
A Learning Error Analysis for Structured Prediction with Approximate Inference—0
On the Robustness of Semantic Segmentation Models to Adversarial AttacksCode0
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing—0
Dialogue Act Recognition via CRF-Attentive Structured Network—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified