2k citations · 7.2k across the 38 of their papers we have counts for
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cs.LG2013★ 48 cited
Unit Tests for Stochastic Optimization
Tom Schaul, Ioannis Antonoglou, David Silver
Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms. A wide variety of such optimization algorithms have been devi…
cs.LG2013★ 17 cited
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients
Tom Schaul, Yann LeCun
Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, wh…