Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning Technologies
Machine learning (ML) frameworks rely heavily on pseudorandom number generators (PRNGs) for tasks such as data shuffling, weight initialization, dropout, and optimization. Yet, the statistical quality and reproducibility of these generators—particularly when integrated into frameworks like PyTorch,...
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| Published in: | International Journal of Data Informatics and Intelligent Computing Vol. 4; no. 3; pp. 23 - 32 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
20.08.2025
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| ISSN: | 2583-6250, 2583-6250 |
| Online Access: | Get full text |
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