The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

ICLR 2019 Jonathan FrankleMichael Carbin

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance... (read more)

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