Computer Science > Machine Learning
arXiv:2311.12727 (cs)
[Submitted on 21 Nov 2023 (v1), last revised 24 Nov 2023 (this version, v2)]
Title:Soft Random Sampling: A Theoretical and Empirical Analysis
View a PDF of the paper titled Soft Random Sampling: A Theoretical and Empirical Analysis, by Xiaodong Cui and 5 other authors
View PDFAbstract:Soft random sampling (SRS) is a simple yet effective approach for efficient training of large-scale deep neural networks when dealing with massive data. SRS selects a subset uniformly at random with replacement from the full data set in each epoch. In this paper, we conduct a theoretical and empirical analysis of SRS. First, we analyze its sampling dynamics including data coverage and occupancy. Next, we investigate its convergence with non-convex objective functions and give the convergence rate. Finally, we provide its generalization performance. We empirically evaluate SRS for image recognition on CIFAR10 and automatic speech recognition on Librispeech and an in-house payload dataset to demonstrate its effectiveness. Compared to existing coreset-based data selection methods, SRS offers a better accuracy-efficiency trade-off. Especially on real-world industrial scale data sets, it is shown to be a powerful training strategy with significant speedup and competitive performance with almost no additional computing cost.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2311.12727 [cs.LG] |
| (orarXiv:2311.12727v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2311.12727 arXiv-issued DOI via DataCite |
Submission history
From: Xiaodong Cui [view email][v1] Tue, 21 Nov 2023 17:03:21 UTC (178 KB)
[v2] Fri, 24 Nov 2023 03:27:31 UTC (178 KB)
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View a PDF of the paper titled Soft Random Sampling: A Theoretical and Empirical Analysis, by Xiaodong Cui and 5 other authors
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