Understanding the difficulty of training deep feedforward neural networks (Xavier Initialization)

Identified variance explosion and vanishing gradients in deep networks, deriving Xavier/Glorot variance-normalized weight initialization based on fan-in and fan-out.

Serves as qualified theoretical and empirical reference for task-multiclass-classification.

Assumptions

• Mathematical convexity, regularity, and empirical consistency hold across problem configurations

Limitations

• Theoretical bounds and benchmark saturation characteristics vary across model architectures and training scales