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An Overview of the Data-Loader Landscape: Numerical Results Cont.
by The Serialization PublicationJune 4th, 2024
Too Long; Didn't Read
In this paper, researchers highlight dataloaders as key to improving ML training, comparing libraries for functionality, usability, and performance.
Authors:
(1) Iason Ofeidis, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven {Equal contribution};
(2) Diego Kiedanski, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven {Equal contribution};
(3) Leandros TassiulasLevon Ghukasyan, Activeloop, Mountain View, CA, USA, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven.
Table of Links
- Abstract and Intro
- Dataloaders
- Experimental Setup
- Numerical Results
- Discussion
- Related Work
- Conclusion, Acknowledgments, and References
- A. Numerical Results Cont.
A. NUMERICAL RESULTS CONT.
In this appendix, we include a collection of plots for which we did not have space in the core pages of the article.
This paper is available on arxiv under CC 4.0 license.
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