398 citations · 1.7k across the 20 of their papers we have counts for
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stat.ML2016★ 174 cited
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…
cs.NE2016★ 342 cited
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez +5
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In th…