The following diagram shows the typical quantity of “shared” self-interest heads, in the subnetworks for all GLUE tasks pairs. For instance, QQP and MRPC are closer in terms of job formulation than QQP and MNLI, but the “good” subnetworks share heads in each circumstances. But there is a theorem that even depth-two networks can approximate any continuous function F. If the assumptions were the identical, then their theorem would imply any continuous function F is w.h.p. approximated by some subnetwork of a depth-4 network.
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In this Appendix, we present these metrics across the complete education approach, along with further metrics on the partnership among weights in the full network and weights in the winning and random tickets. He, Y., Zhang, X., and Sun, J. Channel pruning for accelerating quite deep neural networks. In International Conference on Computer Vision , volume 2, pp. 6, 2017.
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