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Juncheng Gu

2 accepted papers

2022

SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training

NeurIPS 2022accept

Data parallelism across multiple machines is widely adopted for accelerating distributed deep learning, but it is hard to achieve linear speedup due to the heavy communication. In this paper, we propose SAPipe, a performant system that pushes the training speed of data parallelism to its fullest ext…

Cited by 16SourcePDFScholar
2020

Efficient Adversarial Training With Transferable Adversarial Examples

CVPR 2020poster

Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. I…

Cited by 156PDFcodeScholar