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In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements.
Classification parameters for the emission-line spectra of extragalactic objects
Jack A. Baldwin, Mark M. Phillips, and Roberto Terlevich · 1981
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Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures
Gustavo Deco and Wilfried Brauer · 1995
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Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
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Monte Carlo Statistical Methods
Christian Robert and George Casella · 2004
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Markov Chain Monte Carlo: Stochastic simulation for Bayesian inference
Dani Gamerman and Hedibert F Lopes · 2006
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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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Hallmarks of cancer: The next generation
Douglas Hanahan and Robert A. Weinberg · 2011
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Structure and feedback in 30 Doradus. II. Structure and chemical abundances
Eric W. Pellegrini, Jack A. Baldwin, and Gary J. Ferland · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Model based inversion for deriving maps of histological parameters characteristic of cancer from ex-vivo multispectral images of the colon
Ela Claridge and Dzena Hidovic-Rowe · 2013
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Theoretical evolution of optical strong lines across cosmic time
Lisa J. Kewley, Michael A. Dopita, Claus Leitherer, Romeel Davé, Tiantian Yuan, Mark Allen, Brent Groves, and Ralph Sutherland · 2013
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High-dimensional probability estimation with deep density models
Oren Rippel and Ryan Prescott Adams · 2013
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Approximate bayesian computation
Mikael Sunnåker, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, and Christophe Dessimoz · 2013
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A family of nonparametric density estimation algorithms
E. G. Tabak and Cristina V. Turner · 2013
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Approximate bayesian computation (abc) gives exact results under the assumption of model error
Richard David Wilkinson · 2013
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NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Medical hyperspectral imaging: a review
Guolan Lu and Baowei Fei · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Bayesian convolutional neural networks with Bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani · 2015
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MADE: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Flow-GAN: Combining maximum likelihood and adversarial learning in generative models
Aditya Grover, Manik Dhar, and Stefano Ermon · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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PixelCNN models with auxiliary variables for natural image modeling
Alexander Kolesnikov and Christoph H. Lampert · 2017
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Fundamentals and recent developments in approximate bayesian computation
Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, and Jukka Corander · 2017
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Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Conditional image generation with PixelCNN decoders
Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
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Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
George Papamakarios and Iain Murray · 2016
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Radiative transfer with polaris: I. analysis of magnetic fields through synthetic dust continuum polarization measurements
Stefan Reissl, Robert Brauer, and Sebastian Wolf · 2016
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Physical processes in the interstellar medium
Ralf S. Klessen and Simon C. O. Glover · 2016
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Improving variational auto-encoders using householder flow
Jakub M Tomczak and Max Welling · 2016
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George Papamakarios, Iain Murray, and Theo Pavlakou · 2017
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Winds and radiation in unison: A new semi-analytic feedback model for cloud dissolution
Daniel Rahner, Eric W. Pellegrini, Simon C. O. Glover, and Ralf S. Klessen · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Learning disentangled representations with semi-supervised deep generative models
N Siddharth, Brooks Paige, Jan-Willem Van de Meent, Alban Desmaison, and Philip HS Torr · 2017
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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
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Physiological Parameter Estimation from Multispectral Images Unleashed
Sebastian J. Wirkert, Anant S. Vemuri, Hannes G. Kenngott, Sara Moccia, Michael Götz, Benjamin F. B. Mayer, Klaus H. Maier-Hein, Daniel S. Elson, and Lena Maier-Hein · 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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Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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i-RevNet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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Training generative reversible networks
R.T. Schirrmeister, P. Chraba̧szcz, F. Hutter, and T. Ball · 2018
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Invertible autoencoder for domain adaptation
Yunfei Teng, Anna Choromanska, and Mariusz Bojarski · 2018
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Conditional density estimation with bayesian normalising flows
Brian L Trippe and Richard E Turner · 2018
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