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Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
Learning representations by back-propagating errors
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Multilayer feedforward networks are universal approximators
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Backpropagation applied to handwritten zip code recognition
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
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An information-maximization approach to blind separation and blind deconvolution
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Natural gradient works efficiently in learning
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The MNIST database of handwritten digits, 1998
Yann LeCun, Corinna Cortes, and Christopher JC Burges · 1998
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
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One-step neural network inversion with pdf learning and emulation
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New Riemannian metrics for improvement of convergence speed in ICA based learning algorithms
Stefano Squartini, Francesco Piazza, and Ali Shawker · 2005
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Evaluating derivatives: principles and techniques of algorithmic differentiation
Andreas Griewank and Andrea Walther · 2008
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Large scale variational inference and experimental design for sparse generalized linear models
Matthias W Seeger and Hannes Nickisch · 2008
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Graphical models, exponential families, and variational inference
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Optimization algorithms on matrix manifolds
P-A Absil, Robert Mahony, and Rodolphe Sepulchre · 2009
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Density estimation by dual ascent of the log-likelihood
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Silvere Bonnabel · 2013
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Oren Rippel and Ryan Prescott Adams · 2013
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Neural networks: a systematic introduction
Raúl Rojas · 2013
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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FFJORD: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
Aapo Hyvärinen, Hiroaki Sasaki, and Richard E Turner · 2018
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i-RevNet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Aapo Hyvarinen and Hiroshi Morioka · 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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Improving variational auto-encoders using Householder flow
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Glow: Generative flow with invertible 1x1 convolutions
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Invertibility of convolutional generative networks from partial measurements
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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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Invertible residual networks
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Tian Qi Chen and David K Duvenaud · 2019
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Invertible convolutional networks
Marc Finzi, Pavel Izmailov, Wesley Maddox, Polina Kirichenko, and Andrew Gordon Wilson · 2019
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The Incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, and Bernhard Schölkopf · 2019
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Emerging convolutions for generative normalizing flows
Emiel Hoogeboom, Rianne van den Berg, and Max Welling · 2019
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Invertible convolutional flow
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Normalizing flows: Introduction and ideas
Ivan Kobyzev, Simon Prince, and Marcus A Brubaker · 2019
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A review of automatic differentiation and its efficient implementation
Charles C Margossian · 2019
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Computational complexity of mathematical operations — Wikipedia, the free encyclopedia
Wikipedia · 2020
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