  The representation of functions by artificial neural networks depends on a
large number of parameters in a non-linear fashion. Suitable parameters of
these are found by minimizing a 'loss functional', typically by stochastic
gradient descent (SGD) or an advanced SGD-based algorithm.
  In a continuous time model for SGD with noise that follows the 'machine
learning scaling', we show that in a certain noise regime, the optimization
algorithm prefers 'flat' minima of the objective function in a sense which is
different from the flat minimum selection of continuous time SGD with
homogeneous noise.
