78 citations · 191 across the 19 of their papers we have counts for
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cs.LG2017★ 78 cited
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Lukas Balles, Philipp Hennig
The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: fo…
cs.LG2017★ 26 cited
Early Stopping without a Validation Set
Maren Mahsereci, Lukas Balles, Christoph Lassner +1
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good…