19 citations · 41 across the 13 of their papers we have counts for
17 papers
Positive-weight moment matching at NNLO and beyond
Benoît Assi, Rene Poncelet, James Whitehead
Parton-shower simulations at state-of-the-art perturbative accuracy carry many negative event weights, which dilute their statistical power. For many purposes, this makes them unus…
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.…
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…
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…
Load Balancing for Skewed Streams on Heterogeneous Cluster
Muhammad Anis Uddin Nasir, Hiroshi Horii, Marco Serafini +4
Streaming applications frequently encounter skewed workloads and execute on heterogeneous clusters. Optimal resource utilization in such adverse conditions becomes a challenge, as…
Fully Dynamic Algorithm for Top- Densest Subgraphs
Muhammad Anis Uddin Nasir, Aristides Gionis, Gianmarco De Francisci Morales +1
Given a large graph, the densest-subgraph problem asks to find a subgraph with maximum average degree. When considering the top- version of this problem, a naïve solution is to…
Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Muhammad Anis Uddin Nasir, Sarunas Girdzijauskas, Nicolas Kourtellis
Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such…