  It is shown that for deep neural networks, a single wide layer of width $N+1$
($N$ being the number of training samples) suffices to prove the connectivity
of sublevel sets of the training loss function. In the two-layer setting, the
same property may not hold even if one has just one neuron less (i.e. width $N$
can lead to disconnected sublevel sets).
