  A paper published in the CVPR 2019 conference outlines a new technique called
'kervolution' used in a new type of augmented convolutional neural network
(CNN) called a 'kervolutional neural network' (KNN). The paper asserts that
KNNs achieve faster convergence and higher accuracies than CNNs. This "mini
paper" will further examine the findings in the original paper and perform a
more in depth analysis of the KNN architecture. This will be done by analyzing
the impact of hyper parameters (specifically the learning rate) on KNNs versus
CNNs, experimenting with other types of kervolution operations not tested in
the original paper, a more rigourous statistical analysis of accuracies and
convergence times and additional theoretical analysis. The accompanying code is
publicly available.
