347 citations · 402 across the 10 of their papers we have counts for
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cs.LG2020★ 9 cited
Parallel Training of Deep Networks with Local Updates
Michael Laskin, Luke Metz, Seth Nabarro +5
Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to g…
cs.LG2020★ 6 cited
Improving Neural Network Training in Low Dimensional Random Bases
Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi
Stochastic Gradient Descent (SGD) has proven to be remarkably effective in optimizing deep neural networks that employ ever-larger numbers of parameters. Yet, improving the efficie…