Björn Weghenkel

RUB
INI
Alumni
Björn Weghenkel

bjoern.weghenkel@ini.rub.de

Theory of Neural Systems

2018

Slowness as a Proxy for Temporal Predictability: An Empirical Comparison
Weghenkel, B., & Wiskott, L.
Neural Computation, 30(5), 1151–1179

@article{WeghenkelWiskott2018, author = {Weghenkel, Björn and Wiskott, Laurenz}, title = {Slowness as a Proxy for Temporal Predictability: An Empirical Comparison}, journal = {Neural Computation}, volume = {30}, number = {5}, pages = {1151–1179}, year = {2018}, doi = {10.1162/neco_a_01070}, }

Weghenkel, B., & Wiskott, L.. (2018). Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Computation, 30(5), 1151–1179. http://doi.org/10.1162/neco_a_01070

2017

Graph-based predictable feature analysis
Weghenkel, B., Fischer, A., & Wiskott, L.
Machine Learning, 1–22

preprint

@article{WeghenkelFischerWiskott2017, author = {Weghenkel, Björn and Fischer, Asja and Wiskott, Laurenz}, title = {Graph-based predictable feature analysis}, journal = {Machine Learning}, pages = {1–22}, year = {2017}, doi = {10.1007/s10994-017-5632-x}, }

Weghenkel, B., Fischer, A., & Wiskott, L.. (2017). Graph-based predictable feature analysis. Machine Learning, 1–22. http://doi.org/10.1007/s10994-017-5632-x

2016

Graph-based Predictable Feature Analysis
Weghenkel, B., Fischer, A., & Wiskott, L.
e-print arXiv:1602.00554v1

@misc{WeghenkelFischerWiskott2016, author = {Weghenkel, Björn and Fischer, Asja and Wiskott, Laurenz}, title = {Graph-based Predictable Feature Analysis}, howpublished = {e-print arXiv:1602.00554v1}, month = {February}, year = {2016}, }

Weghenkel, B., Fischer, A., & Wiskott, L.. (2016, February). Graph-based Predictable Feature Analysis. e-print arXiv:1602.00554v1. Retrieved from http://arxiv.org/abs/1602.00554v1

2014

Learning predictive partitions for continuous feature spaces
Weghenkel, B., & Wiskott, L.
In Proc. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 23-25, Bruges, Belgium (pp. 577–582)

pdf

@inproceedings{WeghenkelWiskott2014, author = {Weghenkel, Björn and Wiskott, Laurenz}, title = {Learning predictive partitions for continuous feature spaces}, booktitle = {Proc. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 23-25, Bruges, Belgium}, pages = {577–582}, year = {2014}, }

Weghenkel, B., & Wiskott, L.. (2014). Learning predictive partitions for continuous feature spaces. In Proc. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 23-25, Bruges, Belgium (pp. 577–582).

2012

Predictable Feature Analysis

Richthofer, S., Weghenkel, B., & Wiskott, L.

In Frontiers in Computational Neuroscience

@inproceedings{RichthoferWeghenkelWiskott2012,
	author		=	{Richthofer, Stefan and Weghenkel, Björn and Wiskott, Laurenz},
	title		=	{Predictable Feature Analysis},
	booktitle	=	{Proc. Bernstein Conference on Computational Neuroscience, Sep 12–14, Munich, Germany},
	volume		=	{},
	year		=	{2012},
	doi		=	{10.3389/conf.fncom.2012.55.00120},
}

2010

Building a Side Channel Based Disassembler
Eisenbarth, T., Paar, C., & Weghenkel, B.
In M. L. Gavrilova, Tan, C. J. K., & Moreno, E. D. (Eds.), Transactions on Computational Science X: Special Issue on Security in Computing, Part I (pp. 78–99) Berlin, Heidelberg: Springer Berlin Heidelberg

@inbook{EisenbarthPaarWeghenkel2010, author = {Eisenbarth, Thomas and Paar, Christof and Weghenkel, Björn}, title = {Building a Side Channel Based Disassembler}, editor = {Gavrilova, Marina L. and Tan, C. J. Kenneth and Moreno, Edward David}, pages = {78–99}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, year = {2010}, doi = {10.1007/978-3-642-17499-5_4}, }

Eisenbarth, T., Paar, C., & Weghenkel, B.. (2010). Building a Side Channel Based Disassembler. In M. L. Gavrilova, Tan, C. J. K., & Moreno, E. D. (Eds.), Transactions on Computational Science X: Special Issue on Security in Computing, Part I (pp. 78–99). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-642-17499-5_4

Summer Term 2015

Lab courses

Scientific Computing with Python

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