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우리는 단순한 통계적 지표인 그라디언트 잡음 척도가 다양한 과제에서 신경망 훈련의 병렬화 가능성을 예측한다는 것을 발견했습니다. 복잡한 작업은 보통 노이...
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We’ve discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks. Since complex tasks tend to have noisier gradients, increasingly large batch sizes are likely to become useful in the future, removing one potential limit to further growth of AI systems. More broadly, these results show that neural network training need not be considered a mysterious art, but can be rigorized and systematized.
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