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Gaussian-binary restricted Boltzmann machines for modeling natural image statisticsMelchior, J., Wang, N., & Wiskott, L.PLOS ONE, 12(2), 1–24
@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}, }
Melchior, J., Wang, N., & Wiskott, L.. (2017). Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. PLOS ONE, 12(2), 1–24. http://doi.org/10.1371/journal.pone.01710152014
Learning natural image statistics with variants of restricted Boltzmann machinesWang, N.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 statisticsWang, N., Melchior, J., & Wiskott, L.(Vol. 1401.5900) arXiv.org e-Print archive@techreport{WangMelchiorWiskott2014, author = {Wang, Nan and Melchior, Jan and Wiskott, Laurenz}, title = {Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics}, year = {2014}, }
Wang, N., Melchior, J., & Wiskott, L.. (2014). Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics (Vol. 1401.5900). arXiv.org e-Print archive. Retrieved from http://arxiv.org/abs/1401.59002013
How to Center Binary Restricted Boltzmann MachinesMelchior, J., Fischer, A., Wang, N., & Wiskott, L.(Vol. 1311.1354) arXiv.org e-Print archive@techreport{MelchiorFischerWangEtAl2013, author = {Melchior, Jan and Fischer, Asja and Wang, Nan and Wiskott, Laurenz}, title = {How to Center Binary Restricted Boltzmann Machines}, year = {2013}, }
Melchior, J., Fischer, A., Wang, N., & Wiskott, L.. (2013). How to Center Binary Restricted Boltzmann Machines (Vol. 1311.1354). arXiv.org e-Print archive. Retrieved from http://arxiv.org/pdf/1311.1354.pdfModeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machinesWang, N., Jancke, D., & Wiskott, L.arXiv preprint arXiv:1312.6108@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 ImagesWang, N., Melchior, J., & Wiskott, L.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 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.
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