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 191–200 of 639 papers

TitleStatusHype
Feature Noising for Log-Linear Structured Prediction—0
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation—0
A Study of State Aliasing in Structured Prediction with RNNs—0
Energy Disaggregation via Discriminative Sparse Coding—0
Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning—0
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three—0
Entropy-Based Latent Structured Output Prediction—0
A Study of Latent Structured Prediction Approaches to Passage Reranking—0
Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces—0
A Learning Error Analysis for Structured Prediction with Approximate Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified