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 ("lottery 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 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 ("lottery 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 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 ("
lotterywinning 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.