Fetching the paper…
Reading the bibliography…
Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors.
Influence of the atmospheric and instrumental dispersion on the brightness distribution in a galaxy
J. L. Sérsic · 1963
Earlier work this paper cites.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
Earlier work this paper cites.
Semilinear Gravitational Lens Inversion
S. J. Warren and S. Dye · 2003
Earlier work this paper cites.
Shapelets - I. A method for image analysis
Alexandre Refregier · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
Earlier work this paper cites.
A Bayesian analysis of regularized source inversions in gravitational lensing
S. H. Suyu, P. J. Marshall, M. P. Hobson, and R. D. Blandford · 2006
Earlier work this paper cites.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2006
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
Earlier work this paper cites.
Bayesian strong gravitational-lens modelling on adaptive grids: objective detection of mass substructure in Galaxies
S. Vegetti and L. V. E. Koopmans · 2008
Earlier work this paper cites.
Kaare Brandt Petersen, Michael Syskind Pedersen, et al · 2008
Earlier work this paper cites.
Strong Lensing by Galaxies
Tommaso Treu · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
Earlier work this paper cites.
Numerical Solution of Stochastic Differential Equations
P.E. Kloeden and E. Platen · 2011
Earlier work this paper cites.
Astropy: A community Python package for astronomy
Astropy Collaboration, T. P. Robitaille, E. J. Tollerud, P. Greenfield, M. Droettboom, E. Bray, T. Aldcroft, M. Davis, A. Ginsburg, A. M. Price-Whelan, W. E. Kerzendorf, A. Conley, N. Crighton, K. Barbary, D. Muna, H. Ferguson, F. Grollier, M. M. Parikh, P. H. Nair, H. M. Unther, C. Deil, J. Woillez, S. Conseil, R. Kramer, J. E. H. Turner, L. Singer, R. Fox, B. A. Weaver, V. Zabalza, Z. I. Edwards, K. Azalee Bostroem, D. J. Burke, A. R. Casey, S. M. Crawford, N. Dencheva, J. Ely, T. Jenness, K. Labrie, P. L. Lim, F. Pierfederici, A. Pontzen, A. Ptak, B. Refsdal, M. Servillat, and O. Streicher · 2013
Earlier work this paper cites.
What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Yoshua Bengio, Eric Laufer, Guillaume Alain, and Jason Yosinski · 2014
Cited alongside, same era.
Adaptive semi-linear inversion of strong gravitational lens imaging
J. W. Nightingale and S. Dye · 2015
Cited alongside, same era.
Gravitational Lens Modeling with Basis Sets
Simon Birrer, Adam Amara, and Alexandre Refregier · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Cited alongside, same era.
ALMA Imaging and Gravitational Lens Models of South Pole Telescope—Selected Dusty, Star-Forming Galaxies at High Redshifts
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Later among the works it cites.
AR-DAE: towards unbiased neural entropy gradient estimation
Jae Hyun Lim, Aaron C. Courville, Christopher J. Pal, and Chin-Wei Huang · 2020
Later among the works it cites.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
Later among the works it cites.
SLITronomy: towards a fully wavelet-based strong lensing inversion technique
A. Galan, A. Peel, R. Joseph, F. Courbin, and J. L Starck · 2021
Later among the works it cites.
The Intrinsic Scatter of Galaxy Scaling Relations
Connor Stone, Stéphane Courteau, and Nikhil Arora · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. S. Spilker, D. P. Marrone, M. Aravena, M. Béthermin, M. S. Bothwell, J. E. Carlstrom, S. C. Chapman, T. M. Crawford, C. de Breuck, C. D. Fassnacht, A. H. Gonzalez, T. R. Greve, Y. Hezaveh, K. Litke, J. Ma, M. Malkan, K. M. Rotermund, M. Strandet, J. D. Vieira, A. Weiss, and N. Welikala · 2016
Cited alongside, same era.
Jupyter notebooks ? a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, Carol Willing, and Jupyter development team · 2016
Cited alongside, same era.
Variational walkback: Learning a transition operator as a stochastic recurrent net
Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, and Yoshua Bengio · 2017
Cited alongside, same era.
Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks
Warren R. Morningstar, Yashar D. Hezaveh, Laurence Perreault Levasseur, Roger D. Blandford, Philip J. Marshall, Patrick Putzky, and Risa H. Wechsler · 2018
Cited alongside, same era.
The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package
Astropy Collaboration, A. M. Price-Whelan, B. M. Sipőcz, H. M. Günther, P. L. Lim, S. M. Crawford, S. Conseil, D. L. Shupe, M. W. Craig, N. Dencheva, A. Ginsburg, J. T. Vand erPlas, L. D. Bradley, D. Pérez-Suárez, M. de Val-Borro, T. L. Aldcroft, K. L. Cruz, T. P. Robitaille, E. J. Tollerud, C. Ardelean, T. Babej, Y. P. Bach, M. Bachetti, A. V. Bakanov, S. P. Bamford, G. Barentsen, P. Barmby, A. Baumbach, K. L. Berry, F. Biscani, M. Boquien, K. A. Bostroem, L. G. Bouma, G. B. Brammer, E. M. Bray, H. Breytenbach, H. Buddelmeijer, D. J. Burke, G. Calderone, J. L. Cano Rodríguez, M. Cara, J. V. M. Cardoso, S. Cheedella, Y. Copin, L. Corrales, D. Crichton, D. D’Avella, C. Deil, É. Depagne, J. P. Dietrich, A. Donath, M. Droettboom, N. Earl, T. Erben, S. Fabbro, L. A. Ferreira, T. Finethy, R. T. Fox, L. H. Garrison, S. L. J. Gibbons, D. A. Goldstein, R. Gommers, J. P. Greco, P. Greenfield, A. M. Groener, F. Grollier, A. Hagen, P. Hirst, D. Homeier, A. J. Horton, G. Hosseinzadeh, L. Hu, J. S. Hunkeler, Ž. Ivezić, A. Jain, T. Jenness, G. Kanarek, S. Kendrew, N. S. Kern, W. E. Kerzendorf, A. Khvalko, J. King, D. Kirkby, A. M. Kulkarni, A. Kumar, A. Lee, D. Lenz, S. P. Littlefair, Z. Ma, D. M. Macleod, M. Mastropietro, C. McCully, S. Montagnac, B. M. Morris, M. Mueller, S. J. Mumford, D. Muna, N. A. Murphy, S. Nelson, G. H. Nguyen, J. P. Ninan, M. Nöthe, S. Ogaz, S. Oh, J. K. Parejko, N. Parley, S. Pascual, R. Patil, A. A. Patil, A. L. Plunkett, J. X. Prochaska, T. Rastogi, V. Reddy Janga, J. Sabater, P. Sakurikar, M. Seifert, L. E. Sherbert, H. Sherwood-Taylor, A. Y. Shih, J. Sick, M. T. Silbiger, S. Singanamalla, L. P. Singer, P. H. Sladen, K. A. Sooley, S. Sornarajah, O. Streicher, P. Teuben, S. W. Thomas, G. R. Tremblay, J. E. H. Turner, V. Terrón, M. H. van Kerkwijk, A. de la Vega, L. L. Watkins, B. A. Weaver, J. B. Whitmore, J. Woillez, V. Zabalza, and Astropy Contributors · 2018
Cited alongside, same era.
Data-driven reconstruction of gravitationally lensed galaxies using recurrent inference machines
Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh, Roger Blandford, Phil Marshall, Patrick Putzky, Thomas D. Rueter, Risa Wechsler, and Max Welling · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Introduction to Gravitational Lensing: With Python Examples , volume 956
Massimo Meneghetti · 2021
Later among the works it cites.
Strong-lensing source reconstruction with variationally optimized Gaussian processes
Konstantin Karchev, Adam Coogan, and Christoph Weniger · 2022
Closest in time.
Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machines
Alexandre Adam, Laurence Perreault-Levasseur, and Yashar Hezaveh · 2022
Closest in time.
Strong Lensing Source Reconstruction Using Continuous Neural Fields
Siddharth Mishra-Sharma and Ge Yang · 2022
Closest in time.
Realistic galaxy image simulation via score-based generative models
Michael J Smith, James E Geach, Ryan A Jackson, Nikhil Arora, Connor Stone, and Stéphane Courteau · 2022
Closest in time.
Probabilistic Mass Mapping with Neural Score Estimation
Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, and Tim Schrabback · 2022
Closest in time.
Diffusion models as plug-and-play priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras · 2022
Closest in time.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Closest in time.
Probabilistic Machine Learning: An introduction
Kevin P. Murphy · 2022
Closest in time.