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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 71–80 of 135 papers

TitleStatusHype
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Understanding Generalization via Leave-One-Out Conditional Mutual Information—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
VLSI Hypergraph Partitioning with Deep Learning—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Estimating class separability of text embeddings with persistent homology—0
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Transductive Learning Is Compact—0
Learning Graph Neural Networks with Positive and Unlabeled Nodes—0
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