  Adaptive optimization methods, which perform local optimization with a metric
constructed from the history of iterates, are becoming increasingly popular for
training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We
show that for simple overparameterized problems, adaptive methods often find
drastically different solutions than gradient descent (GD) or stochastic
gradient descent (SGD). We construct an illustrative binary classification
problem where the data is linearly separable, GD and SGD achieve zero test
error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to
half. We additionally study the empirical generalization capability of adaptive
methods on several state-of-the-art deep learning models. We observe that the
solutions found by adaptive methods generalize worse (often significantly
worse) than SGD, even when these solutions have better training performance.
These results suggest that practitioners should reconsider the use of adaptive
methods to train neural networks.

Machine Learning;Machine Learning