  Determinantal Point Processes (DPPs) provide an elegant and versatile way to
sample sets of items that balance the point-wise quality with the set-wise
diversity of selected items. For this reason, they have gained prominence in
many machine learning applications that rely on subset selection. However,
sampling from a DPP over a ground set of size $N$ is a costly operation,
requiring in general an $O(N^3)$ preprocessing cost and an $O(Nk^3)$ sampling
cost for subsets of size $k$. We approach this problem by introducing DPPNets:
generative deep models that produce DPP-like samples for arbitrary ground sets.
We develop an inhibitive attention mechanism based on transformer networks that
captures a notion of dissimilarity between feature vectors. We show
theoretically that such an approximation is sensible as it maintains the
guarantees of inhibition or dissimilarity that makes DPPs so powerful and
unique. Empirically, we demonstrate that samples from our model receive high
likelihood under the more expensive DPP alternative.
