Fetching the paper…
Reading the bibliography…
Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science.
Simple conditions for the convergence of the gibbs sampler and metropolis-hastings algorithms
Gareth O Roberts and Adrian FM Smith · 1994
Earlier work this paper cites.
On multivariate goodness-of-fit and two-sample testing
Jerome Friedman · 2004
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
Earlier work this paper cites.
Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Paul Fearnhead and Dennis Prangle · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Bayesian data analysis
Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin · 2013
Earlier work this paper cites.
A more effective coordinate system for parameter estimation of precessing compact binaries from gravitational waves
Benjamin Farr, Evan Ochsner, Will M. Farr, and Richard O’Shaughnessy · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Advanced LIGO
J. Aasi et al · 2015
Earlier work this paper cites.
Advanced Virgo: a second-generation interferometric gravitational wave detector
F. Acernese et al · 2015
Earlier work this paper cites.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
J. Veitch et al · 2015
Earlier work this paper cites.
Observation of Gravitational Waves from a Binary Black Hole Merger
B. P. Abbott et al · 2016
Earlier work this paper cites.
PhenomPv2 – technical notes for the LAL implementation
Alejandro Bohé, Mark Hannam, Sascha Husa, Frank Ohme, Michael Pürrer, and Patricia Schmidt · 2016
Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Earlier work this paper cites.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
Michael U Gutmann and Jukka Corander · 2016
Cited alongside, same era.
ChainConsumer
S. R. Hinton · 2016
Cited alongside, same era.
Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era
Sebastian Khan, Sascha Husa, Mark Hannam, Frank Ohme, Michael Pürrer, Xisco Jimńez Forteza, and Alejandro Bohé · 2016
Cited alongside, same era.
Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
George Papamakarios and Iain Murray · 2016
Cited alongside, same era.
A gravitational-wave standard siren measurement of the Hubble constant
B. P. Abbott et al · 2017
Cited alongside, same era.
Learning summary statistic for approximate bayesian computation via deep neural network
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Mining gold from implicit models to improve likelihood-free inference
Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer · 2020
Later among the works it cites.
Learning Bayesian posteriors with neural networks for gravitational-wave inference
Alvin J. K. Chua and Michele Vallisneri · 2020
Later among the works it cites.
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
Later among the works it cites.
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 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bai Jiang, Tung-yu Wu, Charles Zheng, and Wing H Wong · 2017
Cited alongside, same era.
Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2017
Cited alongside, same era.
GW170817: Measurements of neutron star radii and equation of state
B. P. Abbott et al · 2018
Cited alongside, same era.
Handbook of approximate Bayesian computation
Scott A Sisson, Yanan Fan, and Mark Beaumont · 2018
Cited alongside, same era.
GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
B. P. Abbott et al · 2019
Cited alongside, same era.
BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy
Gregory Ashton et al · 2019
Cited alongside, same era.
Etalumis: Bringing probabilistic programming to scientific simulators at scale
Atilim Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, et al · 2019
Cited alongside, same era.
nflows: normalizing flows in PyTorch, November 2020
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2020
Later among the works it cites.
Likelihood-free mcmc with approximate likelihood ratios
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 2020
Later among the works it cites.
sbi: A toolkit for simulation-based inference
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, and Jakob H. Macke · 2020
Later among the works it cites.
Sampling using su (n) gauge equivariant flows
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S Albergo, Kyle Cranmer, Daniel C Hackett, and Phiala E Shanahan · 2021
Closest in time.
Neural approximate sufficient statistics for implicit models
Yanzhi Chen, Dinghuai Zhang, Michael U Gutmann, Aaron Courville, and Zhanxing Zhu · 2021
Closest in time.
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
Closest in time.
Complete parameter inference for GW150914 using deep learning
Stephen R. Green and Jonathan Gair · 2021
Closest in time.
Benchmarking simulation-based inference
Jan-Matthis Lueckmann, Jan Boelts, David Greenberg, Pedro Goncalves, and Jakob Macke · 2021
Closest in time.
Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
Closest in time.
Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog
R. Abbott et al · 2041
Closest in time.