**GrowNet** is a novel approach to combine the power of gradient boosting to incrementally build complex deep neural networks out of shallow components. It introduces a versatile framework that can readily be adapted for a diverse range of machine learning tasks in a wide variety of domains.

**REINFORCE** is a Monte Carlo variant of a policy gradient algorithm in reinforcement learning. The agent collects samples of an episode using its current policy, and uses it to update the policy parameter $\theta$. Since one full trajectory must be completed to construct a sample space, it is updated as an off-policy algorithm.

$$ \nabla\_{\theta}J\left(\theta\right) = \mathbb{E}\_{\pi}\left[G\_{t}\nabla\_{\theta}\ln\pi\_{\theta}\left(A\_{t}\mid{S\_{t}}\right)\right]$$

Image Credit: [Tingwu Wang](http://www.cs.toronto.edu/~tingwuwang/REINFORCE.pdf)

REINFORCE

GrowNet

Gradient Boosting Neural Networks: GrowNet

The **Rectified Linear Unit N**, or **ReLUN**, is a modification of **[ReLU6](https://paperswithcode.com/method/relu6)** activation function that has trainable parameter **n**.

$$ReLUN(x) = min(max(0, x), n)$$

Source	Gradient Boosting Neural Networks: GrowNet
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com