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We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient extension of Residual Flows.
Use of different Monte Carlo sampling techniques
Herman Kahn · 1955
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The eigenvalues of mega-dimensional matrices
John Skilling · 1989
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1990
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Density networks
David JC MacKay and Mark N Gibbs · 1999
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Linear Algebra and its Applications
David C Lay · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Density estimation by dual ascent of the log-likelihood
Esteban G Tabak, Eric Vanden-Eijnden, et al · 2010
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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High-dimensional probability estimation with deep density models
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Nice: Non-linear independent components estimation
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Understanding and improving convolutional neural networks via concatenated rectified linear units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016
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Improving variational auto-encoders using householder flow
Jakub M Tomczak and Max Welling · 2016
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Sorting out Lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2019
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Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Residual flows for invertible generative modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Block neural autoregressive flow
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2019
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Sylvester normalizing flows for variational inference
Rianne van den Berg, Leonard Hasenclever, Jakub M Tomczak, and Max Welling · 2018
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Regularisation of neural networks by enforcing Lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael Cree · 2018
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Excessive invariance causes adversarial vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2018
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Understanding the limitations of conditional generative models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, and Richard Zemel · 2019
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2019
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Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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Preventing gradient attenuation in Lipschitz constrained convolutional networks
Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B Grosse, and Jörn-Henrik Jacobsen · 2019
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Hybrid Models with Deep and Invertible Features
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The convolution exponential and generalized Sylvester flows
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M Tomczak, and Max Welling · 2020
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