SOTAVerified

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

Showing 110 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
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