Вывод: «Происшествие растяжения резинки»: Особенность оптимизации поздней стадии в семействе методов оптимизации Adam.
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Adaptive Gradient Methods and The Slingshot Effect
Adaptive gradient methods like Adam are crucial for optimizing neural networks, especially when working with Transformers. Our paper introduces the Slingshot Effect, a unique anomaly that occurs in the later stages of training. This phenomenon involves cyclic phase transitions between stable and unstable training, which is identifiable through the cyclic behavior of the last layer’s weights.
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