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 626–639 of 639 papers

TitleStatusHype
Direct Loss Minimization for Structured Prediction—0
Energy Disaggregation via Discriminative Sparse Coding—0
Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces—0
A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction—0
An Introduction to Conditional Random Fields—0
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online LearningCode1
Twin gaussian processes for structured prediction—0
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields—0
Search-based Structured Prediction—0
Robust Near-Isometric Matching via Structured Learning of Graphical Models—0
Structured ranking learning using cumulative distribution networks—0
Partially Observed Maximum Entropy Discrimination Markov Networks—0
Tighter Bounds for Structured Estimation—0
Efficient multiple hyperparameter learning for log-linear models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified