ПРЕПРИНТ
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This study provides a concise comparative overview of two modern directions in machine learning research: optimization of training processes via perturbed equations and ablation-based code generation techniques. It synthesizes existing work, evaluates their methodological contributions, and outlines practical implications for enhancing computational efficiency, model interpretability, and robustness in ML workflows. By referencing recent publications in international conferences, this review aims to contribute to a deeper understanding of current advancements and future challenges in these rapidly evolving fields, ultimately fostering the development of more reliable and transparent AI systems.
Botasheva L. 2025. A Comparative Analysis of Optimization and Ablation Techniques in Modern Machine Learning Systems. PREPRINTS.RU. https://doi.org/10.24108/preprints-3114111
