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We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules.
Iterative solution of nonlinear equations in several variables
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Improved variational inference with inverse autoregressive flow
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Pixel recurrent neural networks
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The reversible residual network: Backpropagation without storing activations
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Masked autoregressive flow for density estimation
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Reversible recurrent neural networks
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On the convergence of adam and beyond
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J. Behrmann, D. D. Will Grathwohl, Ricky T. Q. Chen, and J.-H. Jacobsen · 2019
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E. Hoogeboom, R. Van Den Berg, and M. Welling · 2019
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