Structured Prediction
185 papers with code • 1 benchmarks • 6 datasets
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.
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Use these libraries to find Structured Prediction models and implementationsLatest papers with no code
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction
To bridge this gap, we are the first to leverage linguistic features with a Seq2Seq PLM for OIE.
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
We investigate ways to trade off surrogate loss dimension, the number of problem instances, and restricting the region of consistency in the simplex for multiclass classification.
Structured Language Generation Model for Robust Structure Prediction
Previous work in structured prediction (e. g. NER, information extraction) using single model make use of explicit dataset information, which helps boost in-distribution performance but is orthogonal to robust generalization in real-world situations.
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
We extend the exploit-the-surrogate-gap framework to online structured prediction with \emph{Fenchel--Young losses}, a large family of surrogate losses including the logistic loss for multiclass classification, obtaining finite surrogate regret bounds in various structured prediction problems.
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery
We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery.
An Expression Tree Decoding Strategy for Mathematical Equation Generation
To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others.
"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction
We convert the text to a graph by breaking it into structured elements and connect it with the social network of authors, then structured prediction is done over the elements for identifying perspectives.
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation
Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts.
Emotion-Conditioned Text Generation through Automatic Prompt Optimization
We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure.
On Regularization and Inference with Label Constraints
Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems.