50 citations · 79 across the 7 of their papers we have counts for
Showing 2019Show all
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cs.LG2019★ 7 cited
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
Niv Giladi, Mor Shpigel Nacson, Elad Hoffer +1
Background: Recent developments have made it possible to accelerate neural networks training significantly using large batch sizes and data parallelism. Training in an asynchronous…
cs.LG2019★ 22 cited
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Dan Levi, Liran Gispan, Niv Giladi +1
Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones. In this work…
cs.LG2019★ 50 cited
Augment your batch: better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara +3
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. W…