Markov Chain Neural Networks

CVPRW 2018 pp. 2180-2187

doi:10.1109/CVPRW.2018.00293 /cvprw/2018/awiszus2018cvprw-markov/

Abstract

In this work we present a modified neural network model which is capable to simulate Markov Chains. We show how to express and train such a network, how to ensure given statistical properties reflected in the training data and we demonstrate several applications where the network produces non-deterministic outcomes. One example is a random walker model, e.g. useful for simulation of Brownian motions or a natural Tic-Tac-Toe network which ensures non-deterministic game behavior.

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Text

Awiszus and Rosenhahn. "Markov Chain Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00293

Markdown

[Awiszus and Rosenhahn. "Markov Chain Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/awiszus2018cvprw-markov/) doi:10.1109/CVPRW.2018.00293

BibTeX

@inproceedings{awiszus2018cvprw-markov,
  title     = {{Markov Chain Neural Networks}},
  author    = {Awiszus, Maren and Rosenhahn, Bodo},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {2180-2187},
  doi       = {10.1109/CVPRW.2018.00293},
  url       = {https://mlanthology.org/cvprw/2018/awiszus2018cvprw-markov/}
}