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Structured Prediction
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models
Syntactic Structure Distillation Pretraining For Bidirectional Encoders
Active Imitation Learning with Noisy Guidance
Learning Constraints for Structured Prediction Using Rectifier Networks
Large scale evaluation of importance maps in automatic speech recognition
Learning Composable Energy Surrogates for PDE Order Reduction
The Structured Weighted Violations MIRA
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
Energy Disaggregation with Semi-supervised Sparse Coding
Calibrating Structured Output Predictors for Natural Language Processing
Structured Prediction with Partial Labelling through the Infimum Loss
Learning with Differentiable Perturbed Optimizers
Structured Prediction for Conditional Meta-Learning
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
Uncertainty Estimation in Autoregressive Structured Prediction
On the Discrepancy between Density Estimation and Sequence Generation
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
Torch-Struct: Deep Structured Prediction Library
Sequence Labeling Approach to the Task of Sentence Boundary Detection
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Active Learning in Video Tracking
Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies
Learning Likelihoods with Conditional Normalizing Flows
Effective Decoding in Graph Auto-Encoder using Triadic Closure
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Sato: Contextual Semantic Type Detection in Tables
Graph-to-Graph Transformer for Transition-based Dependency Parsing
Improving Joint Training of Inference Networks and Structured Prediction Energy Networks
Graph Structured Prediction Energy Networks
Co-Generation with GANs using AIS based HMC
Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach
Dr.VOT : Measuring Positive and Negative Voice Onset Time in the Wild
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