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Our results show that late resetting identifies winning tickets for VGG19 and Resnet-18 without having any hyperparameter modification. The green lines in Figure2 show the outcome of applying late resetting to VGG19 at iteration 1,000 and to Resnet-18 at iteration 500 —1.4 epochs each—with the typical hyperparameters for every network. In both situations, iterative pruning with late resetting effectively recovers winning tickets.  
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Summary by CodyWild 8 months agoThis operate attempts to use meta-understanding to find out an update rule for a reinforcement finding out agent. (You can see this above with the outcomes for IMP, Iterative Magnitude Pruning, rolling back to theta-). For each approaches the masks are determined with respect to complete model functionality on a distinct dataset. We are interested in finding the subnetworks that allow BERT to carry out properly on the complete set of 9 GLUE tasks (Wang et al. 2018). GLUE test sets are not publicly out there, and we use the dev sets to each locate the pruning masks and to test the model. Given that we are interested in BERT's 'reasoning strategies' rather than generalization, this approach lets us see the best/worst probable subnetworks for this distinct information.  
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