Pavol Mulinka

According to our database, Pavol Mulinka authored at least 17 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting.

Nikolaos Pavlidis, Vasileios Perifanis, Selim F. Yilmaz, Francesc Wilhelmi, Marco Miozzo, Pavlos S. Efraimidis, Remous-Aris Koutsiamanis, Pavol Mulinka, Paolo Dini

CoRR, 2024

2023

pytorch-widedeep: A flexible package for multimodal deep learning.

Javier Rodriguez Zaurin, Pavol Mulinka

J. Open Source Softw., June, 2023

2022

Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters - A Bayesian Approach.

Pavol Mulinka, Subham Sahoo, Charalampos Kalalas, Pedro H. J. Nardelli

CoRR, 2022

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics.

Alexander Beattie, Pavol Mulinka, Subham Sahoo, Ioannis T. Christou, Charalampos Kalalas, Daniel Gutierrez-Rojas, Pedro H. J. Nardelli

Proceedings of the IEEE International Conference on Communications, 2022

A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks.

Souneil Park, Pavol Mulinka, Diego Perino

Proceedings of the COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, Seattle, WA, USA, 29 June 2022, 2022

2021

Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis.

Sarah Wassermann, Thibaut Cuvelier, Pavol Mulinka, Pedro Casas

IEEE Trans. Netw. Serv. Manag., 2021

Information Processing and Data Visualization in Networked Industrial Systems.

Pavol Mulinka, Charalampos Kalalas, Merim Dzaferagic, Irene Macaluso, Daniel Gutierrez-Rojas, Pedro Henrique Juliano Nardelli, Nicola Marchetti

Proceedings of the 32nd IEEE Annual International Symposium on Personal, 2021

2020

HUMAN - Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation.

Pavol Mulinka, Pedro Casas, Kensuke Fukuda, Lukas Kencl

Proceedings of the 11th International Conference on Network of the Future, 2020

WhatsThat? On the Usage of Hierarchical Clustering for Unsupervised Detection & Interpretation of Network Attacks.

Pavol Mulinka, Kensuke Fukuda, Pedro Casas, Lukas Kencl

Proceedings of the IEEE European Symposium on Security and Privacy Workshops, 2020

2019

ADAM & RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring.

Sarah Wassermann, Thibaut Cuvelier, Pavol Mulinka, Pedro Casas

Proceedings of the 15th International Conference on Network and Service Management, 2019

Continuous and Adaptive Learning over Big Streaming Data for Network Security.

Pavol Mulinka, Pedro Casas, Juan Martin Vanerio

Proceedings of the 2019 IEEE 8th International Conference on Cloud Networking, 2019

Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks.

Pedro Casas, Pavol Mulinka, Juan Martin Vanerio

Proceedings of the 6th ACM Workshop on Moving Target Defense, 2019

2018

Adaptive Network Security through Stream Machine Learning.

Pavol Mulinka, Pedro Casas

Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos, 2018

Stream-based Machine Learning for Network Security and Anomaly Detection.

Pavol Mulinka, Pedro Casas

Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, 2018

Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering.

Pavol Mulinka, Pedro Casas, Lukas Kencl

Proceedings of the 7th IEEE International Conference on Cloud Networking, 2018

2016

Multidimensional cloud latency monitoring and evaluation.

Ondrej Tomanek, Pavol Mulinka, Lukas Kencl

Comput. Networks, 2016

2015

Learning from Cloud latency measurements.

Pavol Mulinka, Lukas Kencl

Proceedings of the IEEE International Conference on Communication, 2015