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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 nonautoregressive fashion and outputs the network parameters of the AF. Like other flow models, HCNAF performs exact likelihood inference. We conduct a number of densitydoi:10.1109/cvpr42600.2020.01456 dblp:conf/cvpr/OhV20 fatcat:7eyqfncr6jgczaldaebmzzgovu