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October 2, 2025Open Access

Toward Efficient Influence Function: Dropout as a Compression Tool

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The method improves efficiency by using dropout to compress gradients, enhancing influence function calculations.
Empirical validation showed significant reductions in memory usage during the influence function computation and gradient compression.
The proposed approach addresses the computational challenges faced by traditional influence function methods in large-scale models.
By preserving essential components of data influence, this method enables broader applications in modern machine learning.

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This novel approach reduces computational costs in machine learning models while preserving data influence.

Zhang et al. (2025) studied this question.

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