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 326–350 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
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
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing—0
Syntax Aware LSTM model for Semantic Role Labeling—0
Spatial Language Understanding with Multimodal Graphs using Declarative Learning based Programming—0
Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision—0
A Joint Sequential and Relational Model for Frame-Semantic Parsing—0
Semi-supervised Structured Prediction with Neural CRF AutoencoderCode0
End-to-End Neural Relation Extraction with Global Optimization—0
Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation—0
Robust Conditional Probabilities—0
Parametric Adversarial Divergences are Good Losses for Generative Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified