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A normalizing flow models a complex probability density as an invertible transformation of a simple density.
Cubic splines for image interpolation and digital filtering
Hou, H. and Andrews, H · 1978
Earlier work this paper cites.
Monotone piecewise cubic interpolation
Fritsch, F. N. and Carlson, R. E · 1980
Earlier work this paper cites.
Flexible regression models with cubic splines
Durrleman, S. and Simon, R · 1989
Earlier work this paper cites.
A simple method for monotonic interpolation in one dimension
Steffen, M · 1990
Earlier work this paper cites.
How to solve a cubic equation, part 5: Back to numerics
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Earlier work this paper cites.
Adam: A method for stochastic optimization
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NICE: Non-linear independent components estimation
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
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SGDR: Stochastic gradient descent with warm restarts
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Earlier work this paper cites.
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Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., and Duvenaud, D. K · 2018
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Neural autoregressive flows
Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
Transformation autoregressive networks
Oliva, J., Dubey, A., Zaheer, M., Poczos, B., Salakhutdinov, R., Xing, E., and Schneider, J · 2018
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WaveGlow: A flow-based generative network for speech synthesis
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Sylvester normalizing flows for variational inference
van den Berg, R., Hasenclever, L., Tomczak, J. M., and Welling, M · 2018
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Block neural autoregressive flow
De Cao, N., Titov, I., and Aziz, W · 2019
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Emerging convolutions for generative normalizing flows
Hoogeboom, E., van den Berg, R., and Welling, M · 2019
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Müller, T., McWilliams, B., Rousselle, F., Gross, M., and Novák, J · 2018
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He, K., Zhang, X., Ren, S., and Sun, J
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Identity mappings in deep residual networks
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Kumar, M., Babaeizadeh, M., Erhan, D., Finn, C., Levine, S., Dinh, L., and Kingma, D · 2019
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