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

Showing 521530 of 639 papers

TitleStatusHype
Transition-Based Syntactic Linearization with Lookahead FeaturesCode0
Learning Fast-Mixing Models for Structured PredictionCode0
Reconstructing the house from the ad: Structured prediction on real estate classifiedsCode0
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic MapsCode0
Learning Hierarchical Interactions at Scale: A Convex Optimization ApproachCode0
Conditional Random Field Autoencoders for Unsupervised Structured PredictionCode0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
Reducing Model Churn: Stable Re-training of Conversational AgentsCode0
Efficient Sub-structured Knowledge DistillationCode0
SparseMAP: Differentiable Sparse Structured InferenceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8Unverified