19 citations · 41 across the 13 of their papers we have counts for
7 papers · 1 filter
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
Edvin Listo Zec, Ebba Ekblom, Martin Willbo +2
We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clie…
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Zekarias T. Kefato, Sarunas Girdzijauskas, Hannes Stärk
Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning h…
Self-supervised Graph Neural Networks without explicit negative sampling
Zekarias T. Kefato, Sarunas Girdzijauskas
Real world data is mostly unlabeled or only few instances are labeled. Manually labeling data is a very expensive and daunting task. This calls for unsupervised learning techniques…
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events
Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video…
Dynamic Embeddings for Interaction Prediction
Zekarias T. Kefato, Sarunas Girdzijauskas, Nasrullah Sheikh +1
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identi…
Gossip and Attend: Context-Sensitive Graph Representation Learning
Zekarias T. Kefato, Sarunas Girdzijauskas
Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the…
Graph Neighborhood Attentive Pooling
Zekarias T. Kefato, Sarunas Girdzijauskas
Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs. Most studies explore the str…