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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 101–110 of 135 papers

TitleStatusHype
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
A Theory for Compressibility of Graph Transformers for Transductive Learning—0
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer—0
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Characterize and Transfer Attention in Graph Neural Networks—0
Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
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