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

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Showing 101–135 of 135 papers

TitleStatusHype
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer—0
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Characterize and Transfer Attention in Graph Neural Networks—0
Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Cross-Graph Learning of Multi-Relational Associations—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
DC Proximal Newton for Non-Convex Optimization Problems—0
Deep Domain Adaptation under Deep Label Scarcity—0
Deep Transductive Semi-supervised Maximum Margin Clustering—0
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents—0
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification—0
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph—0
Generate, Transduct, Adapt: Iterative Transduction with VLMs—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
HONEM: Learning Embedding for Higher Order Networks—0
Hypergraph Pre-training with Graph Neural Networks—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Inductive Graph Neural Networks for Moving Object Segmentation—0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Inductive Two-Layer Modeling with Parametric Bregman Transfer—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
Is Transductive Learning Equivalent to PAC Learning?—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
Localized Complexities for Transductive Learning—0
Low-Rank Graph Contrastive Learning for Node Classification—0
Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning—0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns—0
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