  Event datasets are sequences of events of various types occurring irregularly
over the time-line, and they are increasingly prevalent in numerous domains.
Existing work for modeling events using conditional intensities rely on either
using some underlying parametric form to capture historical dependencies, or on
non-parametric models that focus primarily on tasks such as prediction. We
propose a non-parametric deep neural network approach in order to estimate the
underlying intensity functions. We use a novel multi-channel RNN that optimally
reinforces the negative evidence of no observable events with the introduction
of fake event epochs within each consecutive inter-event interval. We evaluate
our method against state-of-the-art baselines on model fitting tasks as gauged
by log-likelihood. Through experiments on both synthetic and real-world
datasets, we find that our proposed approach outperforms existing baselines on
most of the datasets studied.
