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Quasi-hyperbolic momentum and Adam for deep learning
[article]
2019
arXiv
pre-print
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
arXiv:1810.06801v4
fatcat:tq3iul7mdnhjhjjtq5d7edjacm