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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 1–25 of 135 papers

TitleStatusHype
GraphRouter: A Graph-based Router for LLM SelectionsCode2
wav2graph: A Framework for Supervised Learning Knowledge Graph from SpeechCode2
Boosting Vision-Language Models with TransductionCode2
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event DetectionCode2
Transductive Active Learning: Theory and ApplicationsCode2
Semi-Supervised Domain Generalizable Person Re-IdentificationCode2
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Label Propagation for Zero-shot Classification with Vision-Language ModelsCode1
Few-shot bioacoustic event detection at the DCASE 2022 challengeCode1
View-Consistent Heterogeneous Network on Graphs With Few Labeled NodesCode1
Sparsity-aware neural user behavior modeling in online interaction platformsCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Transductive Learning for Unsupervised Text Style TransferCode1
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
BertGCN: Transductive Text Classification by Combining GCN and BERTCode1
DINE: Domain Adaptation from Single and Multiple Black-box PredictorsCode1
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural NetworksCode1
Embedding Propagation: Smoother Manifold for Few-Shot ClassificationCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
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
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