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 151–160 of 639 papers

TitleStatusHype
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Neuro-Symbolic Constraint Programming for Structured Prediction—0
Set-to-Sequence Methods in Machine Learning: a Review—0
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot ModelsCode0
On the Fundamental Limits of Exact Inference in Structured Prediction—0
Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates—0
Fast rates in structured prediction—0
Randomized Deep Structured Prediction for Discourse-Level Processing—0
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing—0
Overparameterisation and worst-case generalisation: friend or foe?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified