Neural Turing Machine for Sequential Learning of Human Mobility Patterns

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F16%3A00302589" target="_blank" >RIV/68407700:21240/16:00302589 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/document/7727551/" target="_blank" >http://ieeexplore.ieee.org/document/7727551/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727551" target="_blank" >10.1109/IJCNN.2016.7727551</a>

Result language
angličtina
Original language name
Neural Turing Machine for Sequential Learning of Human Mobility Patterns
Original language description
The capacity of recurrent neural networks to learn complex sequential patterns is improving. Recent developments such as Clockwork RNN, Stack RNN, Memory networks and Neural Turing Machine all aim to increase long-term memory capacity of recurrent neural networks. In this study, we investigate properties of Neural Turing Machine, compare it with ensembles of Stack RNN on artificial benchmarks and applied it to learn human mobility patterns. We show, that Neural Turing Machine based predictor outperformed not only n-gram based prediction, but also neighborhood based predictor, that was designed to solve this particular problem. Our models will be deployed in anti-drug police department to predict mobility of suspects.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2016 International Joint Conference on Neural Networks (IJCNN)
ISBN
978-1-5090-0620-5
ISSN
2161-4407
e-ISSN
—
Number of pages
8
Pages from-to
2790-2797
Publisher name
American Institute of Physics and Magnetic Society of the IEEE
Place of publication
San Francisco
Event location
Vancouver
Event date
Jul 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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