Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 |
| Publisher | IEEE Computer Society |
| Pages | 2261-2268 |
| Number of pages | 8 |
| ISBN (electronic) | 9781538661000 |
| Publication status | Published - 17 Dec 2018 |
| Event | 20018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States Duration: 18 Jun 2018 → 23 Jun 2018 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| Volume | 2018-June |
| ISSN (Print) | 2160-7508 |
| ISSN (electronic) | 2160-7516 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. IEEE Computer Society, 2018. p. 2261-2268 8575464 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2018-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Markov chain neural networks
AU - Awiszus, Maren
AU - Rosenhahn, Bodo
PY - 2018/12/17
Y1 - 2018/12/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060859974&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2018.00293
DO - 10.1109/CVPRW.2018.00293
M3 - Conference contribution
AN - SCOPUS:85060859974
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2261
EP - 2268
BT - Proceedings
PB - IEEE Computer Society
T2 - 20018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Y2 - 18 June 2018 through 23 June 2018
ER -