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Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging.
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Gravitational-wave parameter estimation with autoregressive neural network flows
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On multivariate goodness–of–fit and two–sample testing
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Adaptive approximate bayesian computation
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Non-linear regression models for approximate bayesian computation
Michael G. B. Blum and Olivier François · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Simon Wood · 2010
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R. Abbott et al · 2010
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Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R. Williamson, and Gilles Louppe · 2010
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Semi-automatic selection of summary statistics for abc model choice, 2013
Dennis Prangle, Paul Fearnhead, Murray P. Cox, Patrick J. Biggs, and Nigel P. French · 2013
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High-dimensional density ratio estimation with extensions to approximate likelihood computation
Rafael Izbicki, Ann Lee, and Chad Schafer · 2014
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A note on approximating abc-mcmc using flexible classifiers
Kim Pham, David Nott, and Sanjay Chaudhuri · 2014
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Simple model of complete precessing black-hole-binary gravitational waveforms
Mark Hannam, Patricia Schmidt, Alejandro Bohé, Leïla Haegel, Sascha Husa, Frank Ohme, Geraint Pratten, and Michael Pürrer · 2014
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Benjamin Farr, Evan Ochsner, Will M. Farr, and Richard O’Shaughnessy · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Approximating likelihood ratios with calibrated discriminative classifiers
Kyle Cranmer, Juan Pavez, and Gilles Louppe · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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J. Veitch, V. Raymond, B. Farr, W Farr, P. Graff, S. Vitale, et al · 2015
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Fast ε \varepsilon -free inference of simulation models with Bayesian conditional density estimation
Likelihood-free inference by ratio estimation, 2020
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, and Michael U. Gutmann · 2020
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Real-Time Gravitational Wave Science with Neural Posterior Estimation
Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, and Bernhard Schölkopf · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Averting a crisis in simulation-based inference
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, and Gilles Louppe · 2021
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George Papamakarios and Iain Murray · 2016
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
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Observation of Gravitational Waves from a Binary Black Hole Merger
B.P. Abbott et al · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Sebastian Khan, Sascha Husa, Mark Hannam, Frank Ohme, Michael Pürrer, Xisco Jiménez Forteza, and Alejandro Bohé · 2016
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PhenomPv2 – technical notes for the LAL implementation
Alejandro Bohé, Mark Hannam, Sascha Husa, Frank Ohme, Michael Pürrer, and Patricia Schmidt · 2016
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Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J Gonçalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H Macke · 2017
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Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Benchmarking simulation-based inference
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R. Abbott et al · 2021
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Louis Sharrock, Jack Simons, Song Liu, and Mark Beaumont · 2022
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Score modeling for simulation-based inference
Tomas Geffner, George Papamakarios, and Andriy Mnih · 2022
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans · 2022
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
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Building normalizing flows with stochastic interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
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Action matching: A variational method for learning stochastic dynamics from samples
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Randomized conditional flow matching for video prediction
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Contrastive neural ratio estimation
Benjamin K Miller, Christoph Weniger, and Patrick Forré · 2022
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Towards reliable simulation-based inference with balanced neural ratio estimation, 2022
Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, and Gilles Louppe · 2022
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Group equivariant neural posterior estimation
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Chayan Chatterjee, Linqing Wen, Damon Beveridge, Foivos Diakogiannis, and Kevin Vinsen · 2022
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Conditional flow matching: Simulation-free dynamic optimal transport
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Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
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Stochastic interpolants: A unifying framework for flows and diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
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Balancing simulation-based inference for conservative posteriors
Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, and Gilles Louppe · 2023
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Adapting to noise distribution shifts in flow-based gravitational-wave inference
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R. Abbott et al · 2041
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