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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
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Few-shot Novel Category DiscoveryCode0
Accurate and Scalable Graph Neural Networks via Message InvarianceCode0
Generate, Transduct, Adapt: Iterative Transduction with VLMs—0
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks—0
Single-View Graph Contrastive Learning with Soft Neighborhood AwarenessCode0
A Theory for Compressibility of Graph Transformers for Transductive Learning—0
Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media DiscourseCode0
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language ModelsCode0
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