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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 26–50 of 135 papers

TitleStatusHype
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
Transductive Learning Is Compact—0
Low-Rank Graph Contrastive Learning for Node Classification—0
Transductive Active Learning: Theory and ApplicationsCode2
Active Few-Shot Fine-Tuning—0
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer—0
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models—0
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach—0
Regularization and Optimal Multiclass Learning—0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns—0
Fast Online Node Labeling for Very Large GraphsCode0
Estimating class separability of text embeddings with persistent homology—0
Inductive Graph Neural Networks for Moving Object Segmentation—0
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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