Hans Salomonsson

Forskningsingenjör at CSE Verksamhetsstöd

I am passionate about all aspects of machine learning and its dramatic implications for business strategy and society. I have during my career in industry and academia implemented, applied and improved many types of machine learning algorithms. I enjoy taking state of the art machine learning research and adapting it to solve particular research and business problems.

Source: orcid.org
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Showing 8 publications

2023

VEDLIoT: Next generation accelerated AIoT systems and applications

Kevin Mika, René Griessl, Nils Kucza et al
Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023, p. 291-296
Paper in proceeding
2022

VEDLIoT: Very Efficient Deep Learning in IoT

Martin Kaiser, R. Griessl, Nils Kucza et al
Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022, p. 963-968
Paper in proceeding
2019

Bayesian optimization in ab initio nuclear physics

Andreas Ekström, Christian Forssen, Christos Dimitrakakis et al
Journal of Physics G: Nuclear and Particle Physics. Vol. 46 (9)
Journal article
2018

Data-driven algorithm for throughput bottleneck analysis of production systems

Mukund Subramaniyan, Anders Skoogh, Hans Salomonsson et al
Production and Manufacturing Research. Vol. 6 (1), p. 225-246
Journal article
2018

A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

Mukund Subramaniyan, Anders Skoogh, Hans Salomonsson et al
Computers and Industrial Engineering. Vol. 125, p. 533-544
Journal article
2016

An algorithm for data-driven shifting bottleneck detection

Mukund Subramaniyan, Anders Skoogh, Maheshwaran Gopalakrishnan et al
Cogent Engineering. Vol. 3 (1), p. 1-19
Journal article
2016

Word Sense Disambiguation using a Bidirectional LSTM

Mikael Kågebäck, Hans Salomonsson
5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) at the 26th International Conference on Computational Linguistics (COLING 2016)
Paper in proceeding
2016

Machine Learning to predict a ship's fuel consumption in seaways

Wengang Mao, Pär Brandholm, Peter Lenaers et al
13th International Symposium on Practical Design of Ships and Other Floating Structures, PRADS 2016; Hotel Crowne PlazaCopenhagen; Denmark; 4 September 2016 through 8 September 2016
Paper in proceeding

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