Gradient directions and relative inexactness in optimization and machine learning

Artem Vasin

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arXiv · 2407.00667

Gradient directions and relative inexactness in optimization and machine learning

Artem Vasin

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Abstract

In this paper, we investigate the influence of noise giving an estimate of the gradient having a acute angle with the original. Noise amplitude has a relative model. The work offers both theoretical calculations and theorems, as well as experimental results. Classic machine learning problems were chosen as experiments -- linear and logistic regression, computer vision and natural language processing.

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Artem Vasin. 2024-06-30. Gradient directions and relative inexactness in optimization and machine learning. https://arxiv.org/abs/2407.00667

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