  Deep learning models require the configuration of many layers and parameters
in order to get good results. However, there are currently few systematic
guidelines for how to configure a successful model. This means model builders
often have to experiment with different configurations by manually programming
different architectures (which is tedious and time consuming) or rely on purely
automated approaches to generate and train the architectures (which is
expensive). In this paper, we present Rapid Exploration of Model Architectures
and Parameters, or REMAP, a visual analytics tool that allows a model builder
to discover a deep learning model quickly via exploration and rapid
experimentation of neural network architectures. In REMAP, the user explores
the large and complex parameter space for neural network architectures using a
combination of global inspection and local experimentation. Through a visual
overview of a set of models, the user identifies interesting clusters of
architectures. Based on their findings, the user can run ablation and variation
experiments to identify the effects of adding, removing, or replacing layers in
a given architecture and generate new models accordingly. They can also
handcraft new models using a simple graphical interface. As a result, a model
builder can build deep learning models quickly, efficiently, and without manual
programming. We inform the design of REMAP through a design study with four
deep learning model builders. Through a use case, we demonstrate that REMAP
allows users to discover performant neural network architectures efficiently
using visual exploration and user-defined semi-automated searches through the
model space.
