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This paper introduces a new method to build linear flows, by taking the exponential of a linear transformation.
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G. (2019a) · 1906
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Rezende, D. J., Racanière, S., Higgins, I., and Toth, P. (2019) · 1909
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Equivariant flows: sampling configurations for multi-body systems with symmetric energies
Köhler, J., Klein, L., and Noé, F. (2019) · 1910
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E. T., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2019) · 1912
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The padé method for computing the matrix exponential
Arioli, M., Codenotti, B., and Fassino, C. (1996) · 1996
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Woodbury transformations for deep generative flows
Lu, Y. and Huang, B. (2020) · 2002
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Closing the dequantization gap: Pixelcnn as a single-layer flow
Nielsen, D. and Winther, O. (2020) · 2002
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Nonlinear equation solving: A faster alternative to feedforward computation
Song, Y., Meng, C., Liao, R., and Ermon, S. (2020) · 2002
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Predictive sampling with forecasting autoregressive models
Wiggers, A. J. and Hoogeboom, E. (2020) · 2002
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Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later
Moler, C. and Van Loan, C. (2003) · 2003
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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NICE: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y. (2015) · 2015
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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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Variational Inference with Normalizing Flows
Rezende, D. and Mohamed, S. (2015) · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M. (2016) · 2016
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Exploiting cyclic symmetry in convolutional neural networks
Dieleman, S., Fauw, J. D., and Kavukcuoglu, K. (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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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2016) · 2016
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M. (2016) · 2016
Sylvester normalizing flows for variational inference
van den Berg, R., Hasenclever, L., Tomczak, J. M., and Welling, M. (2018) · 2018
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Invertible residual networks
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J. (2019) · 2019
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Residual flows for invertible generative modeling
Chen, T. Q., Behrmann, J., Duvenaud, D., and Jacobsen, J. (2019) · 2019
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Block neural autoregressive flow
De Cao, N., Aziz, W., and Titov, I. (2019) · 2019
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Invertible convolutional networks
Finzi, M., Izmailov, P., Maddox, W., Kirichenko, P., and Wilson, A. G. (2019) · 2019
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Improving normalizing flows via better orthogonal parameterizations
Goliński, A., Lezcano-Casado, M., and Rainforth, T. (2019) · 2019
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Improving variational auto-encoders using householder flow
Tomczak, J. M. and Welling, M. (2016) · 2016
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Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017) · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Murray, I., and Pavlakou, T. (2017) · 2017
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PixelCNN++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P. (2017) · 2017
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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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Regularisation of neural networks by enforcing lipschitz continuity
Gouk, H., Frank, E., Pfahringer, B., and Cree, M. J. (2018) · 2018
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P. (2019) · 2019
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Integer discrete flows and lossless compression
Hoogeboom, E., Peters, J. W. T., van den Berg, R., and Welling, M. (2019b) · 2019
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Sum-of-squares polynomial flow
Jaini, P., Selby, K. A., and Yu, Y. (2019) · 2019
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Invertible convolutional flow
Karami, M., Schuurmans, D., Sohl-Dickstein, J., Dinh, L., and Duckworth, D. (2019) · 2019
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Preventing gradient attenuation in lipschitz constrained convolutional networks
Li, Q., Haque, S., Anil, C., Lucas, J., Grosse, R. B., and Jacobsen, J. (2019) · 2019
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Graph normalizing flows
Liu, J., Kumar, A., Ba, J., Kiros, J., and Swersky, K. (2019) · 2019
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y. (2019) · 2019
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Mintnet: Building invertible neural networks with masked convolutions
Song, Y., Meng, C., and Ermon, S. (2019) · 2019
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