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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
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
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
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
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
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 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
Transductive Learning for Abstractive News Summarization—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
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
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Predicting Strategic Behavior from Free TextCode0
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
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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