  One of the main difficulties in analyzing neural networks is the
non-convexity of the loss function which may have many bad local minima.
  In this paper, we study the landscape of neural networks for binary
classification tasks. Under mild assumptions, we prove that after adding one
special neuron with a skip connection to the output, or one special neuron per
layer, every local minimum is a global minimum.
