Machine Learning, Neural Networks, Optimization, Mathematics.
Rosenbrock’s function with and without the noise: without noise with noise Noise increases as you add dimensions to parameter space. It is easiest to see for Gaussian normal noise. Root-mean-squared error introduced by d-dimensional noise grows approximately as sqrt(d) estimation error For