  The Lottery Ticket Hypothesis (LTH) states that for a reasonably sized neural
network, a sub-network within the same network yields no less performance than
the dense counterpart when trained from the same initialization. This work
investigates the relation between model size and the ease of finding these
sparse sub-networks. We show through experiments that, surprisingly, under a
finite budget, smaller models benefit more from Ticket Search (TS).
