Daniel Kokotajlo Comments On Does The Lottery Ticket Hypothesis Suggest The Scaling Hypothesis?

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This is in agreement with intuition and the earlier theoretical final results for linear models. It need to be noted that the regularising effect could also, at least in portion, be triggered by aspects other than pruning per se. For instance, IMP entails coaching the network various occasions. The linear model is a organic initial step for understanding IMP in neural networks. Nevertheless, this simplification is the principal limitation of our perform. Pruning a weight in a linear model is equivalent to removing a feature.

The green lines in Figure 1 show a replication of SNIP on VGG19 and Resnet-18. The major of Figure three summarizes this behavior for all pruning levels. On the left are convergence occasions in relation to pruning level on the right is accuracy at convergence. When pruned to among 70% and 35%, the typical winning tickets converge at least 22% more quickly and are far more correct than the original network. Additional pruning causes convergence times and accuracy to degrade.

Fine-tuning the final totally-connected layers is mandatory in these transfer understanding scenarios mainly because distinct tasks have various classes. All this signifies that we nevertheless have a lot more concerns than answers about how BERT achieves its outstanding functionality. Do we generally attribute high efficiency on GLUE to linguistic know-how of BERT or dataset artifacts?

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We conduct extensive experiments on benchmark datasets to demonstrate the effectiveness of our pruning technique compared with many state-of-the-art baseline techniques. Figure 7 shows the effect of early turnaround on the accuracy of winning tickets for Resnet-18. The later the turnaround, the much better the accuracy of the winning tickets. Even performing early turnaround at iteration 20,000 nonetheless underperforms pruning at the finish of education. Figure eight shows the magnitude distributions of weights that are sooner or later pruned (a single-shot, 67% pruned) from Resnet-18 at the end of training.

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