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Variance Reduction in Deep Learning: More Momentum is All You Need
[article]
2021
arXiv
pre-print
Variance reduction (VR) techniques have contributed significantly to accelerating learning with massive datasets in the smooth and strongly convex setting (Schmidt et al., 2017; Johnson & Zhang, 2013; Roux et al., 2012). However, such techniques have not yet met the same success in the realm of large-scale deep learning due to various factors such as the use of data augmentation or regularization methods like dropout (Defazio & Bottou, 2019). This challenge has recently motivated the design of
arXiv:2111.11828v1
fatcat:er5drxbzercvbn3q4xgaud4ykm