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Transductive Learning

In this setting, both a labeled training sample and an (unlabeled) test sample are provided at training time. The goal is to predict only the labels of the given test instances as accurately as possible.

Papers

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Showing 81–90 of 135 papers

TitleStatusHype
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time—0
Robust Collective Classification against Structural Attacks—0
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Predicting Strategic Behavior from Free TextCode0
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
Node Masking: Making Graph Neural Networks Generalize and Scale Better—0
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines—0
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis—0
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