Search Results for author:
Found 2 papers, 0 papers with code
Learning Graph Embedding with Adversarial Training Methods
Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.
Ranked #7 on
Node Clustering
on Cora
Privacy-preserving Stochastic Gradual Learning
PRESTIGE bridges private updates of the primal variable (by private sampling) with the gradual curriculum learning (CL).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
