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InvertibleNetworks.jl is a Julia package designed for the scalableimplementation of normalizing flows, a method for density estimation andsampling in high-dimensional distributions. This package excels in memoryefficiency by leveraging the inherent invertibility of normalizing flows, whichsignificantly reduces memory requirements during backpropagation compared toexisting normalizing flow packages that rely on automatic differentiationframeworks. InvertibleNetworks.jl has been adapted for diverse applications,including seismic imaging, medical imaging, and CO2 monitoring, demonstratingits effectiveness in learning high-dimensional distributions.

InvertibleNetworks.jl: A Julia package for scalable normalizing flows