Search Results for author:
Found 3 papers, 2 papers with code
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
In this paper, we propose AugWard (Augmentation-Aware Training with Graph Distance and Consistency Regularization), a novel graph representation learning framework that carefully considers the diversity introduced by graph augmentation.
(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data.
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Psychiatrists diagnose mental disorders via the linguistic use of patients.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
