@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},
}
@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},
}
@article{MelchiorFischerWiskott2016,
author = {Melchior, Jan and Fischer, Asja and Wiskott, Laurenz},
title = {How to Center Deep Boltzmann Machines},
journal = {Journal of Machine Learning Research},
volume = {17},
number = {99},
pages = {1–61},
year = {2016},
}
@inproceedings{KrauseFischerGlasmachersEtAl2013,
author = {Krause, O. and Fischer, A. and Glasmachers, T. and Igel, C.},
title = {Approximation properties of DBNs with binary hidden units and real-valued visible units},
booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
year = {2013},
}
Krause, O., Fischer, A., Glasmachers, T., & Igel, C. (2013). Approximation properties of DBNs with binary hidden units and real-valued visible units. In Proceedings of the International Conference on Machine Learning (ICML).
How to Center Binary Restricted Boltzmann Machines
@techreport{MelchiorFischerWangEtAl2013,
author = {Melchior, Jan and Fischer, Asja and Wang, Nan and Wiskott, Laurenz},
title = {How to Center Binary Restricted Boltzmann Machines},
year = {2013},
}
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.