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Normalizing flows are a class of flexible deep generative models that offer easy likelihood computation.
Universal lipschitz approximation in bounded depth neural networks
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Approximation by superpositions of a sigmoidal function
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Multilayer feedforward networks are universal approximators
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Approximation capabilities of multilayer feedforward networks
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Approximating lipschitz continuous functions with groupsort neural networks
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Representational aspects of depth and conditioning in normalizing flows
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Density estimation by dual ascent of the log-likelihood
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A kernel two-sample test
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A family of nonparametric density estimation algorithms
Tabak, E. G. and Turner, C. V. (2013) · 2013
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On the number of linear regions of deep neural networks
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
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Representation benefits of deep feedforward networks
Telgarsky, M. (2015) · 2015
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Size-noise tradeoffs in generative networks
Bailey, B. and Telgarsky, M. J. (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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Neural autoregressive flows
Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A. C. (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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Sylvester normalizing flows for variational inference
Van Den Berg, R., Hasenclever, L., Tomczak, J. M., and Welling, M. (2018) · 2018
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Sorting out lipschitz function approximation
Anil, C., Lucas, J., and Grosse, R. (2019) · 2019
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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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Universal function approximation by deep neural nets with bounded width and relu activations
Hanin, B. (2017) · 2017
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On the ability of neural nets to express distributions
Lee, H., Ge, R., Ma, T., Risteski, A., and Arora, S. (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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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
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Invertible residual networks
Behrmann, J., Grathwohl, W., Chen, R. T., Duvenaud, D., and Jacobsen, J.-H. (2019) · 2019
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Residual flows for invertible generative modeling
Chen, R. T., Behrmann, J., Duvenaud, D. K., and Jacobsen, J.-H. (2019) · 2019
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Sum-of-squares polynomial flow
Jaini, P., Selby, K. A., and Yu, Y. (2019) · 2019
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Solving ode with universal flows: Approximation theory for flow-based models
Huang, C.-W., Dinh, L., and Courville, A. (2020) · 2020
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The expressive power of a class of normalizing flow models
Kong, Z. and Chaudhuri, K. (2020) · 2020
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On the space-time expressivity of resnets
Müller, J. (2020) · 2020
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Coupling-based invertible neural networks are universal diffeomorphism approximators
Teshima, T., Ishikawa, I., Tojo, K., Oono, K., Ikeda, M., and Sugiyama, M. (2020) · 2020
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