  The purpose of this work was to develop of metrics to assess network
architectures that balance neural network size and task performance. To this
end, the concept of neural efficiency is introduced to measure neural layer
utilization, and a second metric called artificial intelligence quotient (aIQ)
was created to balance neural network performance and neural network
efficiency. To study aIQ and neural efficiency, two simple neural networks were
trained on MNIST: a fully connected network (LeNet-300-100) and a convolutional
neural network (LeNet-5). The LeNet-5 network with the highest aIQ was 2.32%
less accurate but contained 30,912 times fewer parameters than the highest
accuracy network. Both batch normalization and dropout layers were found to
increase neural efficiency. Finally, high aIQ networks are shown to be
memorization and overtraining resistant, capable of learning proper digit
classification with an accuracy of 92.51% even when 75% of the class labels are
randomized. These results demonstrate the utility of aIQ and neural efficiency
as metrics for balancing network performance and size.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition