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InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions.
Zur theorie der orthogonalen funktionensysteme
Alfred Haar · 1909
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Julia: A fast dynamic language for technical computing
Jeff Bezanson, Stefan Karpinski, Viral B Shah, and Alan Edelman · 2012
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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A differentiable programming system to bridge machine learning and scientific computing
Mike Innes, Alan Edelman, Keno Fischer, Chris Rackauckas, Elliot Saba, Viral B Shah, and Will Tebbutt · 2019
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Symmetric block-low-rank layers for fully reversible multilevel neural networks
Bas Peters, Eldad Haber, and Keegan Lensink · 2019
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nflows: normalizing flows in PyTorch, November 2020
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2020
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Bayesflow: Learning complex stochastic models with invertible neural networks
Stefan T Radev, Ulf K Mertens, Andreas Voss, Lynton Ardizzone, and Ullrich Köthe · 2020
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Parameterizing uncertainty by deep invertible networks: An application to reservoir characterization
Gabrio Rizzuti, Ali Siahkoohi, Philipp A Witte, and Felix J Herrmann · 2020
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Hint: Hierarchical invertible neural transport for density estimation and bayesian inference
Jakob Kruse, Gianluca Detommaso, Ullrich Köthe, and Robert Scheichl · 2021
Cited alongside, same era.
Enabling uncertainty quantification for seismic data preprocessing using normalizing flows (nf)—an interpolation example
Rajiv Kumar, Maria Kotsi, Ali Siahkoohi, and Alison Malcolm · 2021
Cited alongside, same era.
Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A Witte, and Felix J Herrmann · 2021
Cited alongside, same era.
Photoacoustic imaging with conditional priors from normalizing flows
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix Johan Herrmann · 2021
Cited alongside, same era.
Fully hyperbolic convolutional neural networks
Keegan Lensink, Bas Peters, and Eldad Haber · 2022
Cited alongside, same era.
Conditional injective flows for bayesian imaging
AmirEhsan Khorashadizadeh, Konik Kothari, Leonardo Salsi, Ali Aghababaei Harandi, Maarten de Hoop, and Ivan Dokmanić · 2023
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Juliadiff/chainrules.jl: v1.58.0, November 2023
Frames White, Michael Abbott, Miha Zgubic, Jarrett Revels, Seth Axen, Alex Arslan, Simeon Schaub, Nick Robinson, Yingbo Ma, Sam, Gaurav Dhingra, Will Tebbutt, David Widmann, Niklas Heim, Niklas Schmitz, Christopher Rackauckas, Carlo Lucibello, Keno Fischer, Rainer Heintzmann, frankschae, Andreas Noack, Alex Robson, cossio, Jerry Ling, mattBrzezinski, Rory Finnegan, Andrei Zhabinski, and Daniel Wennberg · 2023
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Reliable amortized variational inference with physics-based latent distribution correction
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J Herrmann · 2023
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Learned multiphysics inversion with differentiable programming and machine learning
Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J Grady, Ali Siahkoohi, Gabrio Rizzuti, Philipp A Witte, Olav Møyner, Gerard J Gorman, and Felix J Herrmann · 2023
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Framework for Easily Invertible Architectures (FrEIA), 2018-2022
Lynton Ardizzone, Till Bungert, Felix Draxler, Ullrich Köthe, Jakob Kruse, Robert Schmier, and Peter Sorrenson · 2022
Cited alongside, same era.
Point-to-set distance functions for output-constrained neural networks
Bas Peters · 2022
Cited alongside, same era.
Wave-equation-based inversion with amortized variational bayesian inference
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, and Felix J Herrmann · 2022
Cited alongside, same era.
Memory efficient invertible neural networks for 3d photoacoustic imaging
Rafael Orozco, Mathias Louboutin, and Felix J Herrmann · 2022
Cited alongside, same era.
normflows: A pytorch package for normalizing flows
Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2023
Cited alongside, same era.
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard G Baraniuk · 2023
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Solving multiphysics-based inverse problems with learned surrogates and constraints
Ziyi Yin, Rafael Orozco, Mathias Louboutin, and Felix J Herrmann · 2023
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Adjoint operators enable fast and amortized machine learning based bayesian uncertainty quantification
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J Herrmann · 2023
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Amortized normalizing flows for transcranial ultrasound with uncertainty quantification
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix Herrmann · 2023
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Refining amortized posterior approximations using gradient-based summary statistics
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, and Felix J Herrmann · 2023
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Inference of co2 flow patterns–a feasibility study
Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Rafael Orozco, Ziyi Yin, and Felix J Herrmann · 2023
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