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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 76–100 of 135 papers

TitleStatusHype
Transductive Learning for Zero-Shot Object Detection—0
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis—0
Transductive Learning with Multi-class Volume Approximation—0
Transductive Learning with String Kernels for Cross-Domain Text Classification—0
Transductive Multi-class and Multi-label Zero-shot Learning—0
Transductive Multi-label Zero-shot Learning—0
Transductive Non-linear Learning for Chinese Hypernym Prediction—0
Transductive Rademacher Complexity and its Applications—0
Transductive Semi-Supervised Deep Learning using Min-Max Features—0
Transductive Unbiased Embedding for Zero-Shot Learning—0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Understanding Generalization via Leave-One-Out Conditional Mutual Information—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
VLSI Hypergraph Partitioning with Deep Learning—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Estimating class separability of text embeddings with persistent homology—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
Active Few-Shot Fine-Tuning—0
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
Anomaly Detection of Tabular Data Using LLMs—0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
A Theory for Compressibility of Graph Transformers for Transductive Learning—0
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