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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 11–20 of 135 papers

TitleStatusHype
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural NetworksCode1
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
Label Propagation for Zero-shot Classification with Vision-Language ModelsCode1
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Few-shot bioacoustic event detection at the DCASE 2022 challengeCode1
DINE: Domain Adaptation from Single and Multiple Black-box PredictorsCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
Embedding Propagation: Smoother Manifold for Few-Shot ClassificationCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
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