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 176–200 of 639 papers

TitleStatusHype
Estimation from Indirect Supervision with Linear MomentsCode0
Benchmarking Approximate Inference Methods for Neural Structured PredictionCode0
Consistent Structured Prediction with Max-Min Margin Markov NetworksCode0
On the Use of ArXiv as a DatasetCode0
PAC Prediction Sets for Large Language Models of CodeCode0
Promptly Predicting Structures: The Return of InferenceCode0
Reasoning about Actions and State Changes by Injecting Commonsense KnowledgeCode0
Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural NetworksCode0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
ReSeg: A Recurrent Neural Network-based Model for Semantic SegmentationCode0
Efficient Sub-structured Knowledge DistillationCode0
Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFsCode0
SparseMAP: Differentiable Sparse Structured InferenceCode0
Consistent Multitask Learning with Nonlinear Output Relations—0
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection—0
Agreement on Target-bidirectional Neural Machine Translation—0
Computing a partition function of a generalized pattern-based energy over a semiring—0
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing—0
Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation—0
Comparative Analysis between Notations to Classify Named Entities using Conditional Random Fields—0
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields—0
Active Learning in Video Tracking—0
Compact Representation of Uncertainty in Clustering—0
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials—0
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified