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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 1–50 of 135 papers

TitleStatusHype
Boosting Vision-Language Models with TransductionCode2
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event DetectionCode2
GraphRouter: A Graph-based Router for LLM SelectionsCode2
Semi-Supervised Domain Generalizable Person Re-IdentificationCode2
wav2graph: A Framework for Supervised Learning Knowledge Graph from SpeechCode2
Transductive Active Learning: Theory and ApplicationsCode2
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
View-Consistent Heterogeneous Network on Graphs With Few Labeled NodesCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
BertGCN: Transductive Text Classification by Combining GCN and BERTCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
Label Propagation for Zero-shot Classification with Vision-Language ModelsCode1
DINE: Domain Adaptation from Single and Multiple Black-box PredictorsCode1
Sparsity-aware neural user behavior modeling in online interaction platformsCode1
Few-shot bioacoustic event detection at the DCASE 2022 challengeCode1
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural NetworksCode1
Transductive Learning for Unsupervised Text Style TransferCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
Embedding Propagation: Smoother Manifold for Few-Shot ClassificationCode1
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
Fast Online Node Labeling for Very Large GraphsCode0
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot LearningCode0
Distributed representations of graphs for drug pair scoringCode0
Unsupervised Tube Extraction Using Transductive Learning and Dense TrajectoriesCode0
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language ModelsCode0
Identifying Key Sentences for Precision Oncology Using Semi-Supervised LearningCode0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
Single-View Graph Contrastive Learning with Soft Neighborhood AwarenessCode0
Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial ActivationsCode0
Structure-Aware Consensus Network on Graphs with Few Labeled NodesCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image ClassifiersCode0
Label Propagation for Deep Semi-supervised LearningCode0
Learning to learn via Self-CritiqueCode0
Predicting Strategic Behavior from Free TextCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
Few-shot Novel Category DiscoveryCode0
Fast Few-Shot Classification by Few-Iteration Meta-LearningCode0
Accurate and Scalable Graph Neural Networks via Message InvarianceCode0
Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media DiscourseCode0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification—0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents—0
Inductive Graph Neural Networks for Moving Object Segmentation—0
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
Deep Transductive Semi-supervised Maximum Margin Clustering—0
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