| Stream-based machine learning for network security and anomaly detection P Mulinka, P Casas Proceedings of the 2018 workshop on big data analytics and machine learning …, 2018 | 58 | 2018 |
| Multidimensional cloud latency monitoring and evaluation O Tomanek, P Mulinka, L Kencl Computer Networks 107, 104-120, 2016 | 34 | 2016 |
| Speaker identification by K-nearest neighbors: Application of PCA and LDA prior to KNN J Kacur, R Vargic, P Mulinka 2011 18th International Conference on Systems, Signals and Image Processing, 1-4, 2011 | 20 | 2011 |
| Adaptive and reinforcement learning approaches for online network monitoring and analysis S Wassermann, T Cuvelier, P Mulinka, P Casas IEEE Transactions on Network and Service Management 18 (2), 1832-1849, 2020 | 17 | 2020 |
| Should i (re) learn or should i go (on)? stream machine learning for adaptive defense against network attacks P Casas, P Mulinka, J Vanerio Proceedings of the 6th ACM Workshop on Moving Target Defense, 79-88, 2019 | 16 | 2019 |
| Learning from Cloud latency measurements P Mulinka, L Kencl 2015 IEEE International Conference on Communication Workshop (ICCW), 1895-1901, 2015 | 12 | 2015 |
| Continuous and adaptive learning over big streaming data for network security P Mulinka, P Casas, J Vanerio 2019 IEEE 8th International Conference on Cloud Networking (CloudNet), 1-4, 2019 | 8 | 2019 |
| ADAM & RAL: Adaptive memory learning and reinforcement active learning for network monitoring S Wassermann, T Cuvelier, P Mulinka, P Casas 2019 15th International Conference on Network and Service Management (CNSM), 1-9, 2019 | 8 | 2019 |
| Hi-Clust: Unsupervised analysis of cloud latency measurements through hierarchical clustering P Mulinka, P Casas, L Kencl 2018 IEEE 7th International Conference on Cloud Networking (CloudNet), 1-7, 2018 | 8 | 2018 |
| Remember the good, forget the bad, do it fast-continuous learning over streaming data P Mulinka, S Wassermann, G Marín, P Casas Continual Learning Workshop at NeurIPS 2018, 2018 | 7 | 2018 |
| pytorch-widedeep: A flexible package for multimodal deep learning JR Zaurin, P Mulinka Journal of Open Source Software 8 (86), 5027, 2023 | 6 | 2023 |
| A robust and explainable data-driven anomaly detection approach for power electronics A Beattie, P Mulink, S Sahoo, IT Christou, C Kalalas, D Gutierrez-Rojas, ... 2022 IEEE International Conference on Communications, Control, and Computing …, 2022 | 6 | 2022 |
| Information processing and data visualization in networked industrial systems P Mulinka, C Kalalas, M Dzaferagic, I Macaluso, DG Rojas, PJ Nardelli, ... 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile …, 2021 | 6 | 2021 |
| HUMAN-Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation P Mulinka, P Casas, K Fukuda, L Kencl 2020 11th International Conference on Network of the Future (NoF), 132-140, 2020 | 5 | 2020 |
| Adaptive network security through stream machine learning P Mulinka, P Casas Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos, 4-5, 2018 | 5 | 2018 |
| Whatsthat? on the usage of hierarchical clustering for unsupervised detection & interpretation of network attacks P Mulinka, K Fukuda, P Casas, L Kencl 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW …, 2020 | 3 | 2020 |
| Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters-A Bayesian Approach P Mulinka, S Sahoo, C Kalalas, PHJ Nardelli 2022 IEEE Energy Conversion Congress and Exposition (ECCE), 1-6, 2022 | 2 | 2022 |
| Federated learning in mobile networks: A comprehensive case study on traffic forecasting N Pavlidis, V Perifanis, SF Yilmaz, F Wilhelmi, M Miozzo, PS Efraimidis, ... IEEE Transactions on Sustainable Computing, 2024 | 1 | 2024 |
| A Large-scale Examination of” Socioeconomic” Fairness in Mobile Networks S Park, P Mulinka, D Perino Proceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and …, 2022 | 1 | 2022 |
| Hierarchické hustotné shlukování a interpretace síťových měření M Pavol České vysoké učení technické v Praze. Vypočetní a informační centrum., 2021 | | 2021 |
Pedro CasasSenior Scientist, Data Science & AI @AIT ViennaE-mailová adresa ověřena na: ait.ac.at
