InvertibleNetworks.jl - Memory efficient deep learning in Julia - Witte, Philipp; Louboutin, Mathias; Siahkoohi, Ali; Herrmann, Felix J.; Rizzuti, Gabrio; Peters, Bas (JuliaCon 2021, 2021)
We present InvertibleNetworks.jl, an open-source package for invertible neural networks and normalizing flows using memory-efficient backpropagation. InvertibleNetworks.jl uses manually implement gradients to take advantage of the invertibility of building blocks, which allows for scaling to large-scale problem sizes. We present the architecture and features of the library and demonstrate its application to a variety of problems ranging from loop unrolling to uncertainty quantification.