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Markov Chain Neural Networks
AbstractIn 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.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Awiszus_2018_CVPR_Workshops,
author = {Awiszus, Maren and Rosenhahn, Bodo},
title = {Markov Chain Neural Networks},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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