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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 81–90 of 135 papers

TitleStatusHype
Low-Rank Graph Contrastive Learning for Node Classification—0
Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning—0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns—0
MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer—0
Minimax Lower Bounds for Realizable Transductive Classification—0
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting—0
Node Masking: Making Graph Neural Networks Generalize and Scale Better—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks—0
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