  Momentum is a simple and widely used trick which allows gradient-based
optimizers to pick up speed along low curvature directions. Its performance
depends crucially on a damping coefficient $\beta$. Large $\beta$ values can
potentially deliver much larger speedups, but are prone to oscillations and
instability; hence one typically resorts to small values such as 0.5 or 0.9. We
propose Aggregated Momentum (AggMo), a variant of momentum which combines
multiple velocity vectors with different $\beta$ parameters. AggMo is trivial
to implement, but significantly dampens oscillations, enabling it to remain
stable even for aggressive $\beta$ values such as 0.999. We reinterpret
Nesterov's accelerated gradient descent as a special case of AggMo and analyze
rates of convergence for quadratic objectives. Empirically, we find that AggMo
is a suitable drop-in replacement for other momentum methods, and frequently
delivers faster convergence.
