He Initialization

He initialization is a specialized weight initialization technique designed for neural networks that use the Rectified Linear Unit (ReLU) activation function. Because ReLU sets negative values to zero, the standard Xavier initialization can lead to a collapse of the signal in deep networks.

He initialization compensates for this by scaling the variance of the initial weights to account for the properties of ReLU. This allows for the training of much deeper networks, which are necessary for modeling the highly non-linear dynamics of crypto markets.

It ensures that the gradients remain healthy and the network can effectively learn from complex patterns in order flow data. This technique is a crucial advancement for building high-performance deep learning models in finance.

By facilitating the training of deeper architectures, it enables the creation of more sophisticated and accurate derivative pricing models. It is a fundamental tool for modern quantitative researchers working with deep neural networks.