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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 121–130 of 135 papers

TitleStatusHype
Generate, Transduct, Adapt: Iterative Transduction with VLMs—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
HONEM: Learning Embedding for Higher Order Networks—0
Hypergraph Pre-training with Graph Neural Networks—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Inductive Graph Neural Networks for Moving Object Segmentation—0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Inductive Two-Layer Modeling with Parametric Bregman Transfer—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
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