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 526–550 of 639 papers

TitleStatusHype
Canonical Correlation Inference for Mapping Abstract Scenes to Text—0
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker—0
Classical Structured Prediction Losses for Sequence to Sequence Learning—0
CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search—0
Closed-Form Training of Mahalanobis Distance for Supervised Clustering—0
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts—0
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss—0
Cocktail Party Processing via Structured Prediction—0
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser—0
Compact Representation of Uncertainty in Clustering—0
Comparative Analysis between Notations to Classify Named Entities using Conditional Random Fields—0
Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation—0
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing—0
Computing a partition function of a generalized pattern-based energy over a semiring—0
Consistent Multitask Learning with Nonlinear Output Relations—0
Constrained Structure Learning for Scene Graph Generation—0
Constructing Information Networks Using One Single Model—0
Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation—0
Cross-Lingual Discriminative Learning of Sequence Models with Posterior Regularization—0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
Deep imitation learning for molecular inverse problems—0
Deep Learning in Lexical Analysis and Parsing—0
Deeply Learning the Messages in Message Passing Inference—0
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation—0
DeepSPIN: Deep Structured Prediction for Natural Language Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified