Abstract
In this paper we analyzed the dynamics of Recurrent Neural Network architectures. We explored the chaotic nature of state-of-the-art Recurrent Neural Networks: Vanilla Recurrent Network and Recurrent Highway Networks. Our experiments showed that they exhibit chaotic behavior in the absence of input data. We also proposed a way of removing chaos from Recurrent Neural Networks. Our findings show that initialization of the weight matrices during the training plays an important role, as initialization with the matrices whose norm is smaller than one will lead to the non-chaotic behavior of the Recurrent Neural Networks. The advantage of the non-chaotic cells is stable dynamics. At the end, we tested our chaos-free version of the Recurrent Highway Networks (RHN) in a real-world application. In the language modeling task, chaos-free versions of RHN perform on par with the original version.
Original language | English |
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Title of host publication | Computational Linguistics and Intelligent Text Processing - 20th International Conference, CICLing 2019, Revised Selected Papers |
Editors | Alexander Gelbukh |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 351-363 |
Number of pages | 13 |
ISBN (Print) | 9783031243363 |
DOIs | |
Publication status | Published - 2023 |
Event | 20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019 - La Rochelle, France Duration: Apr 7 2019 → Apr 13 2019 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13451 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019 |
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Country/Territory | France |
City | La Rochelle |
Period | 4/7/19 → 4/13/19 |
Funding
Acknowledgement. This work has been funded by the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan, IRN AP05133700. The work of Bagdat Myrzakhmetov partially has been funded by the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan under the research grant AP05134272. The authors would like to thank Professor Anastasios Bountis for his valuable feedback.
Keywords
- Chaos theory
- Language modeling
- Recurrent highway networks
- Recurrent neural networks
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science