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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 21–30 of 135 papers

TitleStatusHype
BertGCN: Transductive Text Classification by Combining GCN and BERTCode1
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification—0
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning—0
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents—0
Anomaly Detection of Tabular Data Using LLMs—0
Active Few-Shot Fine-Tuning—0
Deep Transductive Semi-supervised Maximum Margin Clustering—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
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