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Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models.
Bayesianly justifiable and relevant frequency calculations for the applied statistician
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Monte carlo methods of inference for implicit statistical models
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Optimal Transport , volume 338 of Grundlehren Der Mathematischen Wissenschaften
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U-net: Convolutional networks for biomedical image segmentation
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Deep learning
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Nick Jagiella, Dennis Rickert, Fabian J Theis, and Jan Hasenauer · 2017
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Deep sets, 2017
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Validating bayesian inference algorithms with simulation-based calibration
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Automatic posterior transformation for likelihood-free inference
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Analyzing inverse problems with invertible neural networks
Lynton Ardizzone, Jakob Kruse, Sebastian Wirkert, Daniel Rahner, Eric W Pellegrini, Ralf S Klessen, Lena Maier-Hein, Carsten Rother, and Ullrich Köthe · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Benchmarking invertible architectures on inverse problems
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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Flow straight and fast: Learning to generate and transfer data with rectified flow, 2022
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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Truncated proposals for scalable and hassle-free simulation-based inference
Michael Deistler, Pedro J Goncalves, and Jakob H Macke · 2022
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Score matched neural exponential families for likelihood-free inference
Lorenzo Pacchiardi and Ritabrata Dutta · 2022
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Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics
Grace Avecilla, Julie N Chuong, Fangfei Li, Gavin Sherlock, David Gresham, and Yoav Ram · 2022
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Elucidating the design space of diffusion-based generative models
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Gabriel Peyré and Marco Cuturi · 2020
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Machine learning surrogates for molecular dynamics simulations of soft materials
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Estimation of agent-based models using Bayesian deep learning approach of BayesFlow
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Normalizing flows for probabilistic modeling and inference
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Score-based generative modeling through stochastic differential equations
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Gatsbi: Generative adversarial training for simulation-based inference
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