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Normalizing flows have received a great deal of recent attention as they allow flexible generative modeling as well as easy likelihood computation.
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Multilayer feedforward networks are universal approximators
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Approximation capabilities of multilayer feedforward networks
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Nice: Non-linear independent components estimation
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Montufar, G. F., Pascanu, R., Cho, K., and Bengio, Y. (2014) · 2014
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Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H. (2015) · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
Variational inference with orthogonal normalizing flows
Hasenclever, L., M. Tomczak, J., van den Berg, R., and Welling, M. (2017) · 2017
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The expressive power of neural networks: A view from the width
Lu, Z., Pu, H., Wang, F., Hu, Z., and Wang, L. (2017) · 2017
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Easy proof of the jacobian for the n-dimensional polar coordinates
Muleshkov, A. and Nguyen, T. (2017) · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
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On the expressive power of deep neural networks
Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., and Dickstein, J. S. (2017) · 2017
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Size-noise tradeoffs in generative networks
Bailey, B. and Telgarsky, M. J. (2018) · 2018
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Representation benefits of deep feedforward networks
Telgarsky, M. (2015) · 2015
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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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) · 2016
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Improving variational auto-encoders using householder flow
Tomczak, J. M. and Welling, M. (2016) · 2016
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Neural autoregressive distribution estimation
Uria, B., Côté, M.-A., Gregor, K., Murray, I., and Larochelle, H. (2016) · 2016
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Behrmann, J., Duvenaud, D., and Jacobsen, J.-H. (2018) · 2018
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Sylvester normalizing flows for variational inference
Berg, R. v. d., Hasenclever, L., Tomczak, J. M., and Welling, M. (2018) · 2018
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Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K. (2018) · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Betterncourt, J., Sutskever, I., and Duvenaud, D. (2018) · 2018
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A. (2018) · 2018
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Resnet with one-neuron hidden layers is a universal approximator
Lin, H. and Jegelka, S. (2018) · 2018
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Deep diffeomorphic normalizing flows
Salman, H., Yadollahpour, P., Fletcher, T., and Batmanghelich, K. (2018) · 2018
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Monge-ampere flow for generative modeling
Zhang, L., Wang, L., et al. (2018) · 2018
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