5 citations · 6 across the 7 of their papers we have counts for
15 papers
Learning Cellular Coverage from Real Network Configurations using GNNs
Yifei Jin, Marios Daoutis, Sarunas Girdzijauskas +1
Cellular coverage quality estimation has been a critical task for self-organized networks. In real-world scenarios, deep-learning-powered coverage quality estimation methods cannot…
Open World Learning Graph Convolution for Latency Estimation in Routing Networks
Yifei Jin, Marios Daoutis, Sarunas Girdzijauskas +1
Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not abl…
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…
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events
Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the M…
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…
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming
Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker.…