Marius Geitle

• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2020). A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. 31(12), p. 1–28. doi: 10.1002/ett.3924. Full text in Research Archive
• Geitle, Marius & Olsson, Roland (2019). A New Baseline for Automated Hyper-Parameter Optimization. In Nicosia, Giuseppe; Umeton, Renato; Sciacca, Vincenzo; Pardalos, Panos & Giuffrida, Giovanni (Ed.), Machine Learning,Optimization,and Data Science - 5th International Conference, LOD 2019. Springer Nature. ISSN 978-3-030-37598-0. p. 521–530. doi: https:/doi.org/10.1007/978-3-030-37599-7_43.
• Tennebø, Frode & Geitle, Marius (2019). Evaluating Population Based Training on Small Datasets. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Full text in Research Archive Show summary

  Recently, there has been an increased interest in using artificial neural networks in the severely resource-constrained devices found in Internet-of-Things networks, in order to perform actions learned from the raw sensor data gathered by these devices. Unfortunately, training neural networks to achieve optimal prediction accuracy requires tuning multiple hyper-parameters, a process which has traditionally taken many times the computation time of a single training run of the neural network. In this paper, we empirically evaluate the Population Based Training algorithm, a method which simultaneously both trains and tunes a neural network, on datasets of similar size to what we might encounter in an IoT scenario. We determine that the population based training algorithm achieves prediction accuracy comparable to a traditional grid or random search on small datasets, and achieves state-of-the-art results for the Biodeg dataset.

• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost. In Perković, Toni (Eds.), 2019 4thInternational Conference on Smart and Sustainable Technologies(SpliTech). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-953-290-091-0. doi: 10.23919/SpliTech.2019.8783016.
• Geitle, Marius & Olsson, Roland (2017). Using automatic programming to design improved variants of differential evolution. In Bui, Lam Thu (Eds.), 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-0743-5. p. 13–18. doi: 10.1109/IESYS.2017.8233554.
• Geitle, Marius & Olsson, Roland (2017). Improving competitive differential evolution using automatic programming. In Fei, Xiang (Eds.), 2017 4th International Conference on Systems and Informatics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-1107-4. p. 538–545. doi: 10.1109/ICSAI.2017.8248350.

View all works in Cristin

• Geitle, Marius & Olsson, Roland (2019). A New Baseline for Automated Hyper-Parameter Optimization.
• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost.
• Geitle, Marius (2019). Maskinlæring på Høgskolen i Østfold.
• Geitle, Marius (2018). Hva er Big Data - En introduksjon.
• Geitle, Marius (2018). Lære fra data - Muligheter og utfordringer med maskinlæring.
• Geitle, Marius (2018). Hva er Big Data - En introduksjon.
• Geitle, Marius (2017). Hva er Big Data - En introduksjon.
• Geitle, Marius & Olsson, Roland (2017). Using automatic programming to design improved variants of differential evolution.
• Geitle, Marius & Olsson, Roland (2017). Improving competitive differential evolution using automatic programming.

View all works in Cristin