Lot­tery Ticket Hypothesis

TagLast edit: 26 Nov 2021 14:08 UTC by Multicore

The Lottery Ticket Hypothesis claims that neural networks used in machine learning get most of their performance from sub-networks that are already present at initialization that approximate the final policy (“winning tickets”). The training process would, under this model, work by increasing weight on the lottery ticket sub-network and reducing weight on the rest of the network.

The hypothesis was proposed in a paper by Jonathan Frankle and Micheal Carbin of MIT CSAIL.

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