25,436 research outputs found
Neural Autoregressive Distribution Estimation
We present Neural Autoregressive Distribution Estimation (NADE) models, which
are neural network architectures applied to the problem of unsupervised
distribution and density estimation. They leverage the probability product rule
and a weight sharing scheme inspired from restricted Boltzmann machines, to
yield an estimator that is both tractable and has good generalization
performance. We discuss how they achieve competitive performance in modeling
both binary and real-valued observations. We also present how deep NADE models
can be trained to be agnostic to the ordering of input dimensions used by the
autoregressive product rule decomposition. Finally, we also show how to exploit
the topological structure of pixels in images using a deep convolutional
architecture for NADE
HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application for Probabilistic Occupancy Map Forecasting
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.Comment: CVPR 202
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