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 81–90 of 639 papers

TitleStatusHype
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker—0
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts—0
A New Smooth Approximation to the Zero One Loss with a Probabilistic Interpretation—0
A New Recurrent Neural CRF for Learning Non-linear Edge Features—0
A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization—0
Bethe Projections for Non-Local Inference—0
A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling—0
Belief Propagation in Conditional RBMs for Structured Prediction—0
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing—0
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified