  A core problem in machine learning is to learn expressive latent variables
for model prediction on complex data that involves multiple sub-components in a
flexible and interpretable fashion. Here, we develop an approach that improves
expressiveness, provides partial interpretation, and is not restricted to
specific applications. The key idea is to dynamically distance data samples in
the latent space and thus enhance the output diversity. Our dynamic latent
separation method, inspired by atomic physics, relies on the jointly learned
structures of each data sample, which also reveal the importance of each
sub-component for distinguishing data samples. This approach, atom modeling,
requires no supervision of the latent space and allows us to learn extra
partially interpretable representations besides the original goal of a model.
We empirically demonstrate that the algorithm also enhances the performance of
small to larger-scale models in various classification and generation problems.
