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Normalizing flows learn a diffeomorphic mapping between the target and base distribution, while the Jacobian determinant of that mapping forms another real-valued function.
Piecewise rational quadratic interpolation to monotonic data
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Probabilistic principal component analysis
Tipping, M. E. and Bishop, C. M · 1999
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Huang, C., Dinh, L., and Courville, A · 2002
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Training deep nets with sublinear memory cost
Chen, T., Xu, B., Zhang, C., and Guestrin, C · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 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
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Sylvester normalizing flows for variational inference
Generative model with dynamic linear flow
Liao, H., He, J., and Shu, K · 2019
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Neural importance sampling
Müller, T., McWilliams, B., Rousselle, F., Gross, M., and Novák, J · 2019
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
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How to train your neural ode: the world of jacobian and kinetic regularization
Finlay, C., Jacobsen, J.-H., Nurbekyan, L., and Oberman, A · 2020
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Solving ode with universal flows: Approximation theory for flow-based models
Huang, C., Dinh, L., and Courville, A · 2020
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Normalizing flows: An introduction and review of current methods
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van den Berg, R., Hasenclever, L., Tomczak, J., and Welling, M · 2018
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Monge-ampere flow for generative modeling
Zhang, L., Wang, L., et al · 2018
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Invertible residual networks
Behrmann, J., , Grathwohl, W., Chen, Ricky T. Q.and Duvenaud, D., and Jacobsen, J · 2019
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Residual flows for invertible generative modeling
Chen, R. T. Q., Behrmann, J., Duvenaud, D., and Jacobsen, J · 2019
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Dupont, E., Doucet, A., and Teh, Y. W · 2019
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Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2019
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Kobyzev, I., Prince, S., and Brubaker, M · 2020
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Representational aspects of depth and conditioning in normalizing flows
Koehler, F., Mehta, V., and Risteski, A · 2020
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The expressive power of a class of normalizing flow models
Kong, Z. and Chaudhuri, K · 2020
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Gaussianization flows
Meng, C., Song, Y., Song, J., and Ermon, S · 2020
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Ot-flow: Fast and accurate continuous normalizing flows via optimal transport
Onken, D., Fung, S. W., Li, X., and Ruthotto, L · 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
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Potential flow generator with l 2 l_{2} optimal transport regularity for generative models
Yang, L. and Karniadakis, G. E · 2020
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Approximation capabilities of neural ODEs and invertible residual networks
Zhang, H., Gao, X., Unterman, J., and Arodz, T · 2020
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