SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 135 papers

TitleStatusHype
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
Show:102550
← PrevPage 10 of 14Next →

No leaderboard results yet.