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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 101–135 of 135 papers

TitleStatusHype
Selective Transfer Machine for Personalized Facial Action Unit Detection—0
Self-Training: A Survey—0
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms—0
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach—0
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates—0
Smoothed Analysis of Sequential Probability Assignment—0
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time—0
The Benefits and Risks of Transductive Approaches for AI Fairness—0
Towards Adversarial Robustness via Transductive Learning—0
Towards Understanding the Generalization of Graph Neural Networks—0
Transductive Boltzmann Machines—0
Transductive image segmentation: Self-training and effect of uncertainty estimation—0
Transductive Learning for Abstractive News Summarization—0
Transductive Learning for Abstractive News Summarization—0
Label Propagation for Deep Semi-supervised LearningCode0
Identifying Key Sentences for Precision Oncology Using Semi-Supervised LearningCode0
Single-View Graph Contrastive Learning with Soft Neighborhood AwarenessCode0
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
Predicting Strategic Behavior from Free TextCode0
Learning to learn via Self-CritiqueCode0
Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media DiscourseCode0
Accurate and Scalable Graph Neural Networks via Message InvarianceCode0
Fast Few-Shot Classification by Few-Iteration Meta-LearningCode0
Structure-Aware Consensus Network on Graphs with Few Labeled NodesCode0
Unsupervised Tube Extraction Using Transductive Learning and Dense TrajectoriesCode0
Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial ActivationsCode0
Distributed representations of graphs for drug pair scoringCode0
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language ModelsCode0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
Fast Online Node Labeling for Very Large GraphsCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image ClassifiersCode0
Few-shot Novel Category DiscoveryCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot LearningCode0
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
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