[2303.16529] Importance Sampling for Stochastic Gradient Descent in Deep Neural Networks
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Computer Science > Machine Learning

arXiv:2303.16529 (cs)
[Submitted on 29 Mar 2023]

Title:Importance Sampling for Stochastic Gradient Descent in Deep Neural Networks

Authors:Thibault Lahire
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Abstract:Stochastic gradient descent samples uniformly the training set to build an unbiased gradient estimate with a limited number of samples. However, at a given step of the training process, some data are more helpful than others to continue learning. Importance sampling for training deep neural networks has been widely studied to propose sampling schemes yielding better performance than the uniform sampling scheme. After recalling the theory of importance sampling for deep learning, this paper reviews the challenges inherent to this research area. In particular, we propose a metric allowing the assessment of the quality of a given sampling scheme; and we study the interplay between the sampling scheme and the optimizer used.
Comments: 17 pages, 3 figures
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2303.16529 [cs.LG]
  (or arXiv:2303.16529v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2303.16529

Submission history

From: Thibault Lahire [view email]
[v1] Wed, 29 Mar 2023 08:35:11 UTC (577 KB)
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