Attention-Based Guided Structured Sparsity (GSS) (#51)

* Added GSS ("Attention-Based Guided Structured Sparsity of Deep Neural Networks") and an example for ResNet20 channel pruning.
    - The idea is to regularize the variance of the distribution of the parameter structures. Some structures will zero completely and the rest should have a high value leading to a high variance.
    - A new regularizer class, GroupVarianceRegularizer, is used to regularize the group variance (effectively rewarding the loss function for high variance between the groups).
    - When tested on ResNet 20 GSS did not show any improvement over SSL

* Added sample of filter pruning for ResNet20 CIFAR using SSL (Learning Structured Sparsity in Deep Neural Networks)

* Added an example of pruning 45% of the compute (1.8x MAC reduction), while suffering 0.8% accuracy loss on ResNet20 CIFAR

* Added a ResNet50 ImageNet example of L1-Magnitude fine-grained pruning, using an AGP schedule: 46% sparsity with a 0.6% accuracy increase. This is an example of using pruning used as a regularizer.