On the evolution of recurrent neural systems
The evolution of neural network architectures, first of the recurrent type and then with the use of attention technology, is considered. It shows how the approaches changed and how the developers’ experience was enriched. It is important that the neural networks themselves learn to understand the de...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Ukrainian |
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Igor Sikorsky Kyiv Polytechnic Institute
2024-12-01
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| Series: | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï |
| Subjects: | |
| Online Access: | http://journal.iasa.kpi.ua/article/view/322523 |
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| _version_ | 1849324613824151552 |
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| author | Gennadii Abramov Ivan Gushchin Tetiana Sirenka |
| author_facet | Gennadii Abramov Ivan Gushchin Tetiana Sirenka |
| author_sort | Gennadii Abramov |
| collection | DOAJ |
| description | The evolution of neural network architectures, first of the recurrent type and then with the use of attention technology, is considered. It shows how the approaches changed and how the developers’ experience was enriched. It is important that the neural networks themselves learn to understand the developers’ intentions and actually correct errors and flaws in technologies and architectures. Using new active elements instead of neurons expanded the scope of connectionist networks. It led to the emergence of new structures — Kolmogorov–Arnold Networks (KANs), which may become serious competitors to networks with artificial neurons. |
| format | Article |
| id | doaj-art-3dda28e352a2423490f86d48531f2713 |
| institution | Kabale University |
| issn | 1681-6048 2308-8893 |
| language | Ukrainian |
| publishDate | 2024-12-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï |
| spelling | doaj-art-3dda28e352a2423490f86d48531f27132025-08-20T03:48:41ZukrIgor Sikorsky Kyiv Polytechnic InstituteSistemnì Doslìdženâ ta Informacìjnì Tehnologìï1681-60482308-88932024-12-014778510.20535/SRIT.2308-8893.2024.4.06361238On the evolution of recurrent neural systemsGennadii Abramov0https://orcid.org/0000-0003-0333-8819Ivan Gushchin1https://orcid.org/0000-0002-1917-716XTetiana Sirenka2Kherson State Maritime Academy, KhersonV. N. Karazin Kharkiv National University, KharkivV. N. Karazin Kharkiv National University, KharkivThe evolution of neural network architectures, first of the recurrent type and then with the use of attention technology, is considered. It shows how the approaches changed and how the developers’ experience was enriched. It is important that the neural networks themselves learn to understand the developers’ intentions and actually correct errors and flaws in technologies and architectures. Using new active elements instead of neurons expanded the scope of connectionist networks. It led to the emergence of new structures — Kolmogorov–Arnold Networks (KANs), which may become serious competitors to networks with artificial neurons.http://journal.iasa.kpi.ua/article/view/322523recurrent neural networkstransformer technologykans |
| spellingShingle | Gennadii Abramov Ivan Gushchin Tetiana Sirenka On the evolution of recurrent neural systems Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï recurrent neural networks transformer technology kans |
| title | On the evolution of recurrent neural systems |
| title_full | On the evolution of recurrent neural systems |
| title_fullStr | On the evolution of recurrent neural systems |
| title_full_unstemmed | On the evolution of recurrent neural systems |
| title_short | On the evolution of recurrent neural systems |
| title_sort | on the evolution of recurrent neural systems |
| topic | recurrent neural networks transformer technology kans |
| url | http://journal.iasa.kpi.ua/article/view/322523 |
| work_keys_str_mv | AT gennadiiabramov ontheevolutionofrecurrentneuralsystems AT ivangushchin ontheevolutionofrecurrentneuralsystems AT tetianasirenka ontheevolutionofrecurrentneuralsystems |