In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain subnetworks which entirely by chance can be tuned to a similar level of performance as the complete network.[1]
Malach et. al. have proved a stronger version of the hypothesis, which is that a sufficiently overparameterized untuned network will typically contain a subnetwork that is already an approximation to the given goal, even before tuning.[2] A similar result has been proven for the special case of convolutional neural networks.[3]
See also
editReferences
edit- ^ Frankle, Jonathan; Carbin, Michael (2019-03-04). "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks". arXiv:1803.03635 [cs.LG]., published as a conference paper at ICLR 2019.
- ^ Malach, Eran; Yehudai, Gilad; Shalev-Shwartz, Shai; Shamir, Ohad (2020-02-03). "Proving the Lottery Ticket Hypothesis: Pruning is All You Need". arXiv:2002.00585 [cs.LG]. published in Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020
- ^ da Cunha, Arthur; Natale, Emanuele; Viennot, Laurent (2022). "Proving the Strong Lottery Ticket Hypothesis for Convolutional Neural Networks". ICLR 2022 - 10th International Conference on Learning Representations. Virtual, France.
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