Papers › Quasi-hyperbolic momentum and Adam for deep learning

Quasi-hyperbolic momentum and Adam for deep learning

16 Oct 2018ICLR 2019 5arXiv:1810.06801archive 2025-07-28

Jerry Ma, Denis Yarats

Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. Code is immediately available.

Code

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Methods

Introduced by this paper: QHAdam, QHM

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