I'm really curious what effect the common tricks for training have on the smoothness of this landscape: momentum, skip connections, batch/layer/etc normalizatio

I'm really curious what effect the common tricks for training have on the smoothness of this landscape: momentum, skip connections, batch/layer/etc normalization, even model size. I imagine the fractal or chaos is still there, but maybe "smoother" and easier for metalearning to deal with?