19 citations · 41 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Pedestrian Trajectory Prediction with Convolutional Neural Networks
Simone Zamboni, Zekarias Tilahun Kefato, Sarunas Girdzijauskas +2
Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to…
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…