RReLU
Randomized Leaky Rectified Linear Units
**Randomized Leaky Rectified Linear Units**, or **RReLU**, are an activation function that randomly samples the negative slope for activation values. It was first proposed and used in the Kaggle NDSB Competition. During training, $a\_{ji}$ is a random number sampled from a uniform distribution $U\left(l, u\right)$. Formally:

$$ y\_{ji} = x\_{ji} \text{   if } x\_{ji} \geq{0} $$
$$ y\_{ji} = a\_{ji}x\_{ji} \text{   if } x\_{ji} < 0 $$

where

$$\alpha\_{ji} \sim U\left(l, u\right), l < u \text{ and } l, u \in \left[0,1\right)$$

In the test phase, we take average of all the $a\_{ji}$ in training similar to [dropout](https://paperswithcode.com/method/dropout), and thus set $a\_{ji}$ to $\frac{l+u}{2}$ to get a deterministic result. As suggested by the NDSB competition winner, $a\_{ji}$ is sampled from $U\left(3, 8\right)$. 

At test time, we use:

$$ y\_{ji} = \frac{x\_{ji}}{\frac{l+u}{2}} $$
Empirical Evaluation of Rectified Activations in Convolutional Network
https://paperswithcode.com/paper/empirical-evaluation-of-rectified-activations
Empirical Evaluation of Rectified Activations in Convolutional Network