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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
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
GraphRouter: A Graph-based Router for LLM SelectionsCode2
VLSI Hypergraph Partitioning with Deep Learning—0
wav2graph: A Framework for Supervised Learning Knowledge Graph from SpeechCode2
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Structure-Aware Consensus Network on Graphs with Few Labeled NodesCode0
Anomaly Detection of Tabular Data Using LLMs—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
The Benefits and Risks of Transductive Approaches for AI Fairness—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
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