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Normalizing flows have shown great success as general-purpose density estimators.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 1906
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Remarks on a multivariate transformation
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A new hybrid quadratic/bisection algorithm for finding the zero of a nonlinear function without using derivatives
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Stochastic structured variational inference
M. Hoffman and D. Blei · 2015
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Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
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R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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Improved variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
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John Salvatier, Thomas V Wiecki, and Christopher Fonnesbeck · 2016
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J. V. Dillon, I. Langmore, D. Tran, E. Brevdo, S. Vasudevan, D. Moore, B. Patton, A. Alemi, M. Hoffman, and R. A. Saurous · 2017
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Automatic differentiation variational inference
A. Kucukelbir, D. Tran, R. Ranganath, A. Gelman, and D. M. Blei · 2017
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Masked autoregressive flow for density estimation
G. Papamakarios, T. Pavlakou, and I. Murray · 2017
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Deep probabilistic programming
D. Tran, M. D. Hoffman, R. A. Saurous, E. Brevdo, K. Murphy, and D. M Blei · 2017
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Recurrent highway networks
J. Georg Zilly, Rupesh K. S., J. Koutnık, and J. Schmidhuber · 2017
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Glow: Generative flow with invertible 1x1 convolutions
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Automatic reparameterisation of probabilistic programs
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Equivariant flow-based sampling for lattice gauge theory
G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racaniere, D. J. Rezende, and P. E. Shanahan · 2020
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Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. Prince, and M. Brubaker · 2020
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Neural canonical transformation with symplectic flows
S. Li, C. Dong, L. Zhang, and L. Wang · 2020
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Joint distributions for tensorflow probability
D. Piponi, D. Moore, and J. V. Dillon · 2020
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Multivariate probabilistic time series forecasting via conditioned normalizing flows
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Flow-based generative models for markov chain monte carlo in lattice field theory
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Pyro: Deep universal probabilistic programming
E. Bingham, J. P Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, and N. D. Goodman · 2019
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
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Differentiable strong lensing: Uniting gravity and neural nets through differentiable probabilistic programming
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Dark matter subhalos, strong lensing and machine learning
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Structured conditional continuous normalizing flows for efficient amortized inference in graphical models
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Introduction to normalizing flows for lattice field theory
M. S. Albergo, D. Boyda, D. C. Hackett, G. Kanwar, K. Cranmer, S. Racanière, D. J. Rezende, and P. E. Shanahan · 2021
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Normalizing flows as a novel pdf turbulence model
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Estimation of thermodynamic observables in lattice field theories with deep generative models
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E(n) equivariant normalizing flows for molecule generation in 3d
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