Structured Prediction
186 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.
Libraries
Use these libraries to find Structured Prediction models and implementations
MNIST
ListOps
CFQ
SciREX
WIKIOG
WBM
Latest papers
Mixture of Soft Prompts for Controllable Data Generation
derekchen14/mixture_soft_prompts
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Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns.
PAC Prediction Sets for Large Language Models of Code
Given a trained code generation model, our algorithm leverages a programming language's abstract syntax tree to generate a set of programs such that the correct program is in the set with high-confidence.
Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints
lcalem/diva
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Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents.
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
ywyue/roomformer
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CVPR 2023
Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of ordered vertices.
Lifting Weak Supervision To Structured Prediction
Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from a variety of sources.
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
hans66hsu/structured_uncertainty_metrics
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Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics.
Autoregressive Structured Prediction with Language Models
lyutyuh/asp
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Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
Code4Struct: Code Generation for Few-Shot Event Structure Prediction
As a case study, we formulate Event Argument Extraction (EAE) as converting text into event-argument structures that can be represented as a class object using code.
Evidence > Intuition: Transferability Estimation for Encoder Selection
mainlp/logme-nlp
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With the increase in availability of large pre-trained language models (LMs) in Natural Language Processing (NLP), it becomes critical to assess their fit for a specific target task a priori - as fine-tuning the entire space of available LMs is computationally prohibitive and unsustainable.
A Survey of Active Learning for Natural Language Processing
In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP).
