scientificarticle.page.titleprefix Overcoming challenges in artificial intelligence training: data limitations, computational costs and model robustness
| custom.quartile | Вітчизняні фахові наукові видання | |
| dc.contributor.author | Bortnyk, Kateryna | |
| dc.contributor.author | Yaroshchuk, Bohdan | |
| dc.contributor.author | Bahniuk, Nataliia | |
| dc.contributor.author | Pekh, Petro | |
| dc.date.accessioned | 2026-09-29T12:43:52Z | |
| dc.date.issued | 2023-12-16 | |
| dc.description.abstract | This paper explores challenges in AI training, focusing on data limitations, computational costs, and the need for robust models. It discusses innovative solutions like synthetic data generation, efficient neural architectures, and robustness techniques, highlighting the importance of AI model interpretability. | |
| dc.identifier.doi | https://doi.org/10.36910/6775-2524-0560-2023-53-06 | |
| dc.identifier.uri | https://repository.lntu.edu.ua/handle/123456789/6367 | |
| dc.language.iso | en | |
| dc.publisher | Lutsk: LNTU | |
| dc.subject | artificial intelligence | |
| dc.subject | AI training | |
| dc.subject | computational costs | |
| dc.subject | environmental impact | |
| dc.subject | model robustness | |
| dc.subject | interpretability | |
| dc.subject | energy efficiency | |
| dc.subject | AI ethics | |
| dc.subject | sustainable AI | |
| dc.title | Overcoming challenges in artificial intelligence training: data limitations, computational costs and model robustness | |
| dc.type | Article | |
| dspace.entity.type | ScientificArticle | |
| oaire.citation.issue | 53 |