Part 6/10:
This insight gave rise to the "Lottery Ticket Hypothesis." Here, each sub-network represents a potential 'winning ticket' based on its initialization and the random values of its weights. While it's incredibly difficult for a small network to achieve a good initialization due to its limited capacity, a larger model provides a rich array of initial conditions that help small winning sub-networks to emerge. Effectively, larger models provide more chances for success, allowing for improved learning and better generalization.
This revolutionary perspective highlights that the essence of the best-performing models can actually derive from small, compact sub-networks that are exceptionally capable of learning the underlying mechanisms behind the data, rather than merely memorizing it.