  We introduce Hyper-Conditioned Neural Autoregressive Flow (HCNAF); a powerful
universal distribution approximator designed to model arbitrarily complex
conditional probability density functions. HCNAF consists of a neural-net based
conditional autoregressive flow (AF) and a hyper-network that can take large
conditions in non-autoregressive fashion and outputs the network parameters of
the AF. Like other flow models, HCNAF performs exact likelihood inference. We
conduct a number of density estimation tasks on toy experiments and MNIST to
demonstrate the effectiveness and attributes of HCNAF, including its
generalization capability over unseen conditions and expressivity. Finally, we
show that HCNAF scales up to complex high-dimensional prediction problems of
the magnitude of self-driving and that HCNAF yields a state-of-the-art
performance in a public self-driving dataset.

Machine Learning;Robotics;Machine Learning