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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The multivariate generalised von Mises distribution: Inference and applications
Navarro, A. K. W., Frellsen, J., and Turner, R. E · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Quantum theory, groups and representations: An introduction
Woit, P · 2017
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Hyperspherical variational auto-encoders
Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., and Tomczak, J. M · 2018
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Explorations in homeomorphic variational auto-encoding
Falorsi, L., de Haan, P., Davidson, T. R., De Cao, N., Weiler, M., Forré, P., and Cohen, T. S · 2018
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Directional grid maps: modeling multimodal angular uncertainty in dynamic environments
Senanayake, R. and Ramos, F · 2018
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Reparameterizing distributions on Lie groups
Falorsi, L., de Haan, P., Davidson, T. R., and Forré, P · 2019
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Continuous hierarchical representations with Poincaré variational auto-encoders
Mathieu, E., Le Lan, C., Maddison, C. J., Tomioka, R., and Teh, Y. W · 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
Original
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
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Riemannian normalizing flow on variational Wasserstein autoencoder for text modeling
Wang, P. Z. and Wang, W. Y · 2019
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A Wasserstein minimum velocity approach to learning unnormalized models
Wang, Z., Cheng, S., Li, Y., Zhu, J., and Zhang, B · 2019
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