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Normalizing Flows are a promising new class of algorithms for unsupervised learning based on maximum likelihood optimization with change of variables.
Training with noise is equivalent to Tikhonov regularization
Christopher M. Bishop · 1995
Earlier work this paper cites.
Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Scholkopf, Alexander Smola, and Klaus-Robert Muller · 1998
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin De Silva, and John C. Langford · 2000
Earlier work this paper cites.
Image quality assessment: From error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
Earlier work this paper cites.
Regularised nonlinear blind signal separation using sparsely connected network
Wai L. Woo and Satnam S. Dlay · 2005
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
Cited alongside, same era.
Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2013
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, and et al · 2014
Cited alongside, same era.
NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Perceptual image quality assessment using a normalized Laplacian pyramid
Valero Laparra, Johannes Ballé, Alexander Berardino, and Eero P. Simoncelli · 2016
Cited alongside, same era.
Robust hessian locally linear embedding techniques for high-dimensional data
Xianglei Xing, Sidan Du, and Kejun Wang · 2016
Later among the works it cites.
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
Emerging convolutions for generative normalizing flows
Emiel Hoogeboom, Rianne van den Berg, and Max Welling · 2019
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Loss landscapes of regularized linear autoencoders
Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, and Cotton Seed · 2019
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Hybrid models with deep and invertible features
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
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