Winning the Lottery with Continuous Sparsification

10 Dec 2019Pedro SavareseHugo SilvaMichael Maire

The Lottery Ticket Hypothesis conjectures that, for a typically-sized neural network, it is possible to find small sub-networks that, when trained from scratch, match the performance of the dense counterpart given a comparable training budget. The proposed algorithm to search for winning tickets, Iterative Magnitude Pruning, consistently finds sparse sub-networks which train faster and better than the overparameterized models they were extracted from, creating potential applications to problems such as transfer learning... (read more)

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