As a lab, our work on machine learning is focused primarily on understanding the constituent parts of neural networks, individually and in combination, in order to extract the maximum performance with limited data and limited computational research. This is especially important for autonomous navigation in novel environments. A core component of this effort is identifying the unstated assumptions that are built in to common neural network architectures which can affect performance.