It is often convenient to model high-dimensional datasets as probability distributions.

We will often use variational optimization. When the variational family is not made explicit it means that the optimization is over all functions with the correct domain and codomain, e.g.

Determinantal point processes (DPPs) are popular probabilistic models of diversity.

To circumvent the hurdle, in this paper, we present a neural field representation that enables efficient and convenient manipulation of models.