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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 41–50 of 135 papers

TitleStatusHype
Towards Understanding the Generalization of Graph Neural Networks—0
MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer—0
Smoothed Analysis of Sequential Probability Assignment—0
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates—0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
Distributed representations of graphs for drug pair scoringCode0
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Few-shot bioacoustic event detection at the DCASE 2022 challengeCode1
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling—0
Understanding Generalization via Leave-One-Out Conditional Mutual Information—0
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