  Typical deep learning approaches to modeling high-dimensional data often
result in complex models that do not easily reveal a new understanding of the
data. Research in the deep learning field is very actively pursuing new methods
to interpret deep neural networks and to reduce their complexity. An approach
is described here that starts with linear models and incrementally adds
complexity only as supported by the data. An application is shown in which
models that map global temperature and precipitation to years are trained to
investigate patterns associated with changes in climate.
