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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 51–100 of 135 papers

TitleStatusHype
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
View-Consistent Heterogeneous Network on Graphs With Few Labeled NodesCode1
Sparsity-aware neural user behavior modeling in online interaction platformsCode1
Self-Training: A Survey—0
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot LearningCode0
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Transductive Learning for Abstractive News Summarization—0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image ClassifiersCode0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Transductive Learning for Unsupervised Text Style TransferCode1
Semi-Supervised Domain Generalizable Person Re-IdentificationCode2
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
Transductive image segmentation: Self-training and effect of uncertainty estimation—0
Towards Adversarial Robustness via Transductive Learning—0
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph—0
Hypergraph Pre-training with Graph Neural Networks—0
BertGCN: Transductive Text Classification by Combining GCN and BERTCode1
Transductive Learning for Abstractive News Summarization—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
DINE: Domain Adaptation from Single and Multiple Black-box PredictorsCode1
Learning Graph Neural Networks with Positive and Unlabeled Nodes—0
Fast Few-Shot Classification by Few-Iteration Meta-LearningCode0
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time—0
Robust Collective Classification against Structural Attacks—0
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural NetworksCode1
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Predicting Strategic Behavior from Free TextCode0
Embedding Propagation: Smoother Manifold for Few-Shot ClassificationCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
Node Masking: Making Graph Neural Networks Generalize and Scale Better—0
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines—0
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis—0
Transductive Learning for Zero-Shot Object Detection—0
Characterize and Transfer Attention in Graph Neural Networks—0
Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial ActivationsCode0
HONEM: Learning Embedding for Higher Order Networks—0
Learning to learn via Self-CritiqueCode0
Label Propagation for Deep Semi-supervised LearningCode0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
Transductive Learning with String Kernels for Cross-Domain Text Classification—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
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