@article{MelchiorWangWiskott2017,
author = {Melchior, Jan and Wang, Nan and Wiskott, Laurenz},
title = {Gaussian-binary restricted Boltzmann machines for modeling natural image statistics},
journal = {PLOS ONE},
volume = {12},
number = {2},
pages = {1–24},
month = {February},
year = {2017},
doi = {10.1371/journal.pone.0171015},
}
Doctoral thesis, International Graduate School of Neuroscience, Ruhr-Universität Bochum
@phdthesis{Wang2014,
author = {Wang, Nan},
title = {Learning natural image statistics with variants of restricted Boltzmann machines},
school = {International Graduate School of Neuroscience, Ruhr-Universität Bochum},
year = {2014},
}
Wang, N. (2014). Learning natural image statistics with variants of restricted Boltzmann machines. Doctoral thesis, International Graduate School of Neuroscience, Ruhr-Universität Bochum.
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics
@techreport{WangMelchiorWiskott2014,
author = {Wang, Nan and Melchior, Jan and Wiskott, Laurenz},
title = {Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics},
year = {2014},
}
@techreport{MelchiorFischerWangEtAl2013,
author = {Melchior, Jan and Fischer, Asja and Wang, Nan and Wiskott, Laurenz},
title = {How to Center Binary Restricted Boltzmann Machines},
year = {2013},
}
@article{WangJanckeWiskott2013,
author = {Wang, Nan and Jancke, Dirk and Wiskott, Laurenz},
title = {Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines},
journal = {arXiv preprint arXiv:1312.6108},
year = {2013},
}
Wang, N., Jancke, D., & Wiskott, L.. (2013). Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines. arXiv preprint arXiv:1312.6108.
2012
An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images
In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292)
@inproceedings{WangMelchiorWiskott2012,
author = {Wang, Nan and Melchior, Jan and Wiskott, Laurenz},
title = {An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images},
booktitle = {Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium},
pages = {287–292},
year = {2012},
}
Wang, N., Melchior, J., & Wiskott, L.. (2012). An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images. In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292).
The Institut für Neuroinformatik (INI) is a interdisciplinary research unit of the
Ruhr-Universität Bochum. We aim to understand fundamental principles
that characterize how organisms generate behavior and cognition while linked to
their environments through sensory and effector systems. Inspired by insights into
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, theoretical approaches from
physics, mathematics, and computer science, including, in particular,
machine learning, artificial intelligence, autonomous robotics, and
computer vision.