Bibliography (8):

1. ImageNet Large Scale Visual Recognition Challenge

2. Deep Residual Learning for Image Recognition

3. Iterative Magnitude Pruning: Learning both Weights and Connections for Efficient Neural Networks

4. Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

5. 2020-rosenfeld-figure8-sweepingwidthparametercountofcifar10resnettofindoptimallylargemodelforbestpossibleprunedmodel.jpg

6. Wikipedia Bibliography:

   1. Neural scaling law  :

      https://en.wikipedia.org/wiki/Neural_scaling_law

   2. CIFAR-10

   3. Power law