Author

Yurii Pushkarenko

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Open access Aug 2026

АДАПТИВНА РЕГУЛЯРИЗАЦІЯ ЗНАНЬ ДЛЯ ТРАНСФОРМЕРНИХ АРХІТЕКТУР У ЗАДАЧАХ ПОСЛІДОВНОГО НАВЧАННЯ

The subject matter of the article is development of latent representation regularization mechanism in transformer-based architecture under conditions of continuous learning with domain shifts. Modern language models achieve high quality in static learning scenarios, but they remain limited in long-term operation cases,...

Arsentii Kriukov, Yurii Pushkarenko · RADIOELECTRONIC AND COMPUTER... · 0 citations

ADAPTIVE KNOWLEDGE REGULARIZATION FOR CONTINUAL LEARNING IN TRANSFORMER ARCHITECTURES

The conclusions demonstrate that the proposed method achieves lower forgetting, lower perplexity on previously learned domains and a better stability–plasticity trade-off than naive fine-tuning, LoRA and Elastic Weight Consolidation, while requiring comparable computational resources.

Arsentii Kriukov, Yurii Pushkarenko · 0 citations