  The impact of machine learning (ML) in many fields of application is
constrained by lack of annotated data. Among existing tools for ML-assisted
data annotation, one little explored tool type relies on an analogy between the
coordinates of a graphical user interface and the latent space of a neural
network for interaction through direct manipulation. In the present work, we 1)
expand the paradigm by proposing two new analogies: time and force as
reflecting iterations and gradients of network training; 2) propose a network
model for learning a compact graphical representation of the data that takes
into account both its internal structure and user provided annotations; and 3)
investigate the impact of model hyperparameters on the learned graphical
representations of the data, identifying candidate model variants for a future
user study.
