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 311–320 of 639 papers

TitleStatusHype
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses—0
Efficient multiple hyperparameter learning for log-linear models—0
Efficient Multi-Template Learning for Structured Prediction—0
Efficient non-greedy optimization of decision trees—0
Efficient Structured Surrogate Loss and Regularization in Structured Prediction—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
End-to-End Learning for Structured Prediction Energy Networks—0
End-to-End Neural Relation Extraction with Global Optimization—0
Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking—0
Energy Disaggregation via Discriminative Sparse Coding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified