19 citations · 41 across the 13 of their papers we have counts for
5 papers · 1 filter
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.…
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning
Daniel Garcia Bernal, Lodovico Giaretta, Sarunas Girdzijauskas +1
Large scale contextual representation models have significantly advanced NLP in recent years, understanding the semantics of text to a degree never seen before. However, they need…
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…