Simon Hakenes, M.Sc.

RUB
INI
People
Simon Hakenes, M.Sc.

simon.hakenes@ini.rub.de (+49) 234-32-15873

Theory of Machine Learning

Ruhr-Universität Bochum
Institut für Neuroinformatik
Universitätsstraße 150
Building NB, Room NB 3/70

D-44801 Bochum, Germany

(+49) 234-32-15873

01/2019 - today

PhD Student
In "Theory of Machine Learning" group lead by Tobias Glasmachers.

My research is focused on behavior learning in complex 3d environments.
10/2015 - 09/2018

M.Sc. in Electrical Engineering and Information Technology
TU Dortmund University

Thesis: Auxiliary Unsupervised Methods for Deep Reinforcement Learning
10/2012 - 10/2015

B.Sc. in Electrical Engineering and Information Technology
TU Dortmund University

Thesis: Untersuchung von Kamerasensoren in mobiler Hardware auf Verwendbarkeit in der Datenübertragung

2023

Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
Hakenes, S., & Glasmachers, T.
arXiv

link

@misc{HakenesGlasmachers2023, author = {Hakenes, Simon and Glasmachers, Tobias}, title = {Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation}, year = {2023}, doi = {10.48550/ARXIV.2310.10250}, }

Hakenes, S., & Glasmachers, T.. (2023). Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation. arXiv. http://doi.org/10.48550/ARXIV.2310.10250

2019

Boosting Reinforcement Learning with Unsupervised Feature Extraction
Hakenes, S., & Glasmachers, T.
In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (pp. 555–566) Springer International Publishing

link pdf

@incollection{HakenesGlasmachers2019, author = {Hakenes, Simon and Glasmachers, Tobias}, title = {Boosting Reinforcement Learning with Unsupervised Feature Extraction}, booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation}, pages = {555–566}, publisher = {Springer International Publishing}, year = {2019}, doi = {10.1007/978-3-030-30487-4_43}, }

Hakenes, S., & Glasmachers, T.. (2019). Boosting Reinforcement Learning with Unsupervised Feature Extraction. In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (pp. 555–566). Springer International Publishing. http://doi.org/10.1007/978-3-030-30487-4_43

2018

Dense Cloud Classification on Multispectral Satellite Imagery
Wohlfarth, K., Schroer, C., Klab, M., Hakenes, S., Venhaus, M., Kauffmann, S., et al.
In 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) IEEE

@inproceedings{WohlfarthSchroerKlabEtAl2018, author = {Wohlfarth, Kay and Schroer, Christian and Klab, Maximilian and Hakenes, Simon and Venhaus, Maike and Kauffmann, Sonke and Wilhelm, Thorsten and Wohler, Christian}, title = {Dense Cloud Classification on Multispectral Satellite Imagery}, booktitle = {2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)}, publisher = {IEEE}, month = {August}, year = {2018}, doi = {10.1109/prrs.2018.8486379}, }

Wohlfarth, K., Schroer, C., Klab, M., Hakenes, S., Venhaus, M., Kauffmann, S., et al. (2018). Dense Cloud Classification on Multispectral Satellite Imagery. In 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS). IEEE. http://doi.org/10.1109/prrs.2018.8486379

2021

Weiterentwicklung eines strukturierten und differenzierbaren Speichers für effizientes Reinforcement-Lernen
Weber, T.

@masterthesis{Weber2021, author = {Weber, Tobias}, title = {Weiterentwicklung eines strukturierten und differenzierbaren Speichers für effizientes Reinforcement-Lernen}, year = {2021}, }

Weber, T. (2021). Weiterentwicklung eines strukturierten und differenzierbaren Speichers für effizientes Reinforcement-Lernen.

2019

Einzelbildbasierte Wegplanung in virtuellen Umgebungen
Akkiraz, T.

@bachelorthesis{Akkiraz2019, author = {Akkiraz, Tolga}, title = {Einzelbildbasierte Wegplanung in virtuellen Umgebungen}, year = {2019}, }

Akkiraz, T. (2019). Einzelbildbasierte Wegplanung in virtuellen Umgebungen.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de