Marius Geitle

• Nareklishvili, Maria & Geitle, Marius (2024). Deep Ensemble Transformers for Dimensionality Reduction. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 36(2), s. 2091–2102. doi: 10.1109/TNNLS.2024.3357621. Fulltekst i vitenarkiv Vis sammendrag

  We propose deep ensemble transformers (DETs), a fast, scalable approach for dimensionality reduction problems. This method leverages the power of deep neural networks and employs cascade ensemble techniques as its fundamental feature extraction tool. To handle high-dimensional data, our approach employs a flexible number of intermediate layers sequentially. These layers progressively transform the input data into decision tree predictions. To further enhance prediction performance, the output from the final intermediate layer is fed through a feed-forward neural network architecture for final prediction. We derive an upper bound of the disparity between the generalization error and the empirical error and demonstrate that it converges to zero. This highlights the generalizability of our method to parameter estimation and feature selection problems. In our experimental evaluations, DETs outperform existing models in terms of prediction accuracy, representation learning ability, and computational time. Specifically, the method achieves over 95% accuracy in gene expression data and can be trained on average 50% faster than traditional artificial neural networks (ANNs).

• 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), s. 1–28. doi: 10.1002/ett.3924. Fulltekst i vitenarkiv Vis sammendrag

  Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource‐constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end‐devices. Mist computing is a new paradigm that attempts to make use of the more resource‐rich nodes that are closer than Edge nodes to end‐users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive‐scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning‐to‐rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource‐rich nodes, while considering the selection of other nodes.

• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost. I Perković, Toni (Red.), 2019 4thInternational Conference on Smart and Sustainable Technologies(SpliTech). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9789532900910. doi: 10.23919/SpliTech.2019.8783016. Fulltekst i vitenarkiv
• Tennebø, Frode & Geitle, Marius (2019). Evaluating Population Based Training on Small Datasets. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Geitle, Marius & Olsson, Roland (2019). A New Baseline for Automated Hyper-Parameter Optimization. I Nicosia, Giuseppe; Umeton, Renato; Sciacca, Vincenzo; Pardalos, Panos & Giuffrida, Giovanni (Red.), Machine Learning,Optimization,and Data Science - 5th International Conference, LOD 2019. Springer Nature. ISSN 9783030375980. s. 521–530. doi: 10.1007/978-3-030-37599-7_43. Fulltekst i vitenarkiv
• Geitle, Marius & Olsson, Roland (2017). Using automatic programming to design improved variants of differential evolution. I Bui, Lam Thu (Red.), 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538607435. s. 13–18. doi: 10.1109/IESYS.2017.8233554. Fulltekst i vitenarkiv
• Geitle, Marius & Olsson, Roland (2017). Improving competitive differential evolution using automatic programming. I Fei, Xiang (Red.), 2017 4th International Conference on Systems and Informatics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538611074. s. 538–545. doi: 10.1109/ICSAI.2017.8248350. Fulltekst i vitenarkiv

Se alle arbeider i NVA

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

Se alle arbeider i NVA