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Normalizing flows define a probability distribution by an explicit invertible transformation $\boldsymbol{\mathbf{z}}=f(\boldsymbol{\mathbf{x}})$.
A class of methods for solving nonlinear simultaneous equations
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Grundlehren der mathematischen wissenschaften
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
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The eigenvalues of mega-dimensional matrices
John Skilling · 1989
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Learning multiple layers of features from tiny images
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Variational inference with normalizing flows
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Dheeru Dua and Casey Graff · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Neural autoregressive flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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Durk P Kingma and Prafulla Dhariwal · 2018
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Spectral normalization for generative adversarial networks
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Vflow: More expressive generative flows with variational data augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, and Tian Tian · 2020
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Relaxing bijectivity constraints with continuously indexed normalising flows
Rob Cornish, Anthony L Caterini, George Deligiannidis, and Arnaud Doucet · 2020
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How to train your neural ode: the world of jacobian and kinetic regularization
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Residual flows for invertible generative modeling
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
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Stefano Massaroli, Michael Poli, Michelangelo Bin, Jinkyoo Park, Atsushi Yamashita, and Hajime Asama · 2020
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Survae flows: Surjections to bridge the gap between vaes and flows
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Implicit neural representations with periodic activation functions
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