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2020 – today
- 2023
[j3]Javier Rodriguez Zaurin
, Pavol Mulinka:
pytorch-widedeep: A flexible package for multimodal deep learning. J. Open Source Softw. 8(86): 5027 (2023)- [i3]Souneil Park, Pavol Mulinka, Diego Perino:
A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks. CoRR abs/2304.10190 (2023) - 2022
- [c12]Souneil Park, Pavol Mulinka, Diego Perino:
A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks. COMPASS 2022: 248-256 - [c11]Alexander Beattie, Pavol Mulinka, Subham Sahoo, Ioannis T. Christou, Charalampos Kalalas, Daniel Gutierrez-Rojas, Pedro H. J. Nardelli:
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics. SmartGridComm 2022: 296-301 - [i2]Alexander Beattie, Pavol Mulinka, Subham Sahoo, Ioannis T. Christou, Charalampos Kalalas, Daniel Gutierrez-Rojas, Pedro H. J. Nardelli:
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics. CoRR abs/2209.11427 (2022) - [i1]Pavol Mulinka, Subham Sahoo, Charalampos Kalalas, Pedro H. J. Nardelli:
Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters - A Bayesian Approach. CoRR abs/2212.03564 (2022) - 2021
- [j2]Sarah Wassermann, Thibaut Cuvelier, Pavol Mulinka, Pedro Casas:
Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis. IEEE Trans. Netw. Serv. Manag. 18(2): 1832-1849 (2021) - [c10]Pavol Mulinka, Charalampos Kalalas, Merim Dzaferagic, Irene Macaluso, Daniel Gutierrez-Rojas, Pedro Henrique Juliano Nardelli, Nicola Marchetti:
Information Processing and Data Visualization in Networked Industrial Systems. PIMRC 2021: 1-6 - 2020
- [c9]Pavol Mulinka, Kensuke Fukuda, Pedro Casas, Lukas Kencl:
WhatsThat? On the Usage of Hierarchical Clustering for Unsupervised Detection & Interpretation of Network Attacks. EuroS&P Workshops 2020: 574-583 - [c8]Pavol Mulinka, Pedro Casas, Kensuke Fukuda, Lukas Kencl:
HUMAN - Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation. NOF 2020: 132-140
2010 – 2019
- 2019
- [c7]Pedro Casas, Pavol Mulinka, Juan Martin Vanerio:
Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks. MTD@CCS 2019: 79-88 - [c6]Pavol Mulinka, Pedro Casas, Juan Martin Vanerio:
Continuous and Adaptive Learning over Big Streaming Data for Network Security. CloudNet 2019: 1-4 - [c5]Sarah Wassermann, Thibaut Cuvelier, Pavol Mulinka, Pedro Casas:
ADAM & RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring. CNSM 2019: 1-9 - 2018
- [c4]Pavol Mulinka, Pedro Casas, Lukas Kencl:
Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering. CloudNet 2018: 1-7 - [c3]Pavol Mulinka, Pedro Casas:
Stream-based Machine Learning for Network Security and Anomaly Detection. Big-DAMA@SIGCOMM 2018: 1-7 - [c2]Pavol Mulinka, Pedro Casas:
Adaptive Network Security through Stream Machine Learning. SIGCOMM Posters and Demos 2018: 4-5 - 2016
- [j1]Ondrej Tomanek, Pavol Mulinka, Lukas Kencl:
Multidimensional cloud latency monitoring and evaluation. Comput. Networks 107: 104-120 (2016) - 2015
- [c1]Pavol Mulinka, Lukas Kencl:
Learning from Cloud latency measurements. ICC Workshops 2015: 1895-1901
Coauthor Index
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