LARS
LARS
**Layer-wise Adaptive Rate Scaling**, or **LARS**, is a large batch optimization technique.  There are two notable differences between LARS and other adaptive algorithms such as [Adam](https://paperswithcode.com/method/adam) or [RMSProp](https://paperswithcode.com/method/rmsprop): first, LARS uses a separate learning rate for each layer and not for each weight. And second, the magnitude of the update is controlled with respect to the weight norm for better control of training speed.

$$m\_{t} = \beta\_{1}m\_{t-1} + \left(1-\beta\_{1}\right)\left(g\_{t} + \lambda{x\_{t}}\right)$$
$$x\_{t+1}^{\left(i\right)} = x\_{t}^{\left(i\right)}  - \eta\_{t}\frac{\phi\left(|| x\_{t}^{\left(i\right)} ||\right)}{|| m\_{t}^{\left(i\right)} || }m\_{t}^{\left(i\right)} $$
Large Batch Training of Convolutional Networks
https://paperswithcode.com/paper/large-batch-training-of-convolutional
Large Batch Training of Convolutional Networks