
Neural Spline Flows
A normalizing flow models a complex probability density as an invertible...
06/10/2019 ∙ by Conor Durkan, et al. ∙ 5 ∙ shareread it

Masked Autoregressive Flow for Density Estimation
Autoregressive models are among the best performing neural density estim...
05/19/2017 ∙ by George Papamakarios, et al. ∙ 0 ∙ shareread it

Neural Autoregressive Flows
Normalizing flows and autoregressive models have been successfully combi...
04/03/2018 ∙ by ChinWei Huang, et al. ∙ 0 ∙ shareread it

Deep Diffeomorphic Normalizing Flows
The Normalizing Flow (NF) models a general probability density by estima...
10/08/2018 ∙ by Hadi Salman, et al. ∙ 6 ∙ shareread it

SumofSquares Polynomial Flow
Triangular map is a recent construct in probability theory that allows o...
05/07/2019 ∙ by Priyank Jaini, et al. ∙ 0 ∙ shareread it

Block Neural Autoregressive Flow
Normalising flows (NFS) map two density functions via a differentiable b...
04/09/2019 ∙ by Nicola De Cao, et al. ∙ 12 ∙ shareread it

Residual Flows for Invertible Generative Modeling
Flowbased generative models parameterize probability distributions thro...
06/06/2019 ∙ by Ricky T. Q. Chen, et al. ∙ 2 ∙ shareread it
CubicSpline Flows
A normalizing flow models a complex probability density as an invertible transformation of a simple density. The invertibility means that we can evaluate densities and generate samples from a flow. In practice, autoregressive flowbased models are slow to invert, making either density estimation or sample generation slow. Flows based on coupling transforms are fast for both tasks, but have previously performed less well at density estimation than autoregressive flows. We stack a new coupling transform, based on monotonic cubic splines, with LUdecomposed linear layers. The resulting cubicspline flow retains an exact onepass inverse, can be used to generate highquality images, and closes the gap with autoregressive flows on a suite of densityestimation tasks.
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