Batch Normalization

Deep neural networks face training challenges like vanishing gradients and slow convergence. Techniques such as activation functions, Xavier and He weight initialization, and Batch Normalization help …

Researchers at arXiv have identified that Batch Normalization causes gradient skew in dynamic sparse training (DST) methods, leading to slower convergence compared to dense neural network training. Th…