Fetching the paper…
Reading the bibliography…
We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces.
Remarks on Some Nonparametric Estimates of a Density Function
Murray Rosenblatt · 1956
Earlier work this paper cites.
On estimation of a probability density function and mode
Emanuel Parzen · 1962
Earlier work this paper cites.
Density estimation in a topological group
KJ Craswell · 1965
Earlier work this paper cites.
Real and complex analysis, 1974
Rudin Walter · 1974
Earlier work this paper cites.
Neural operator: Graph kernel network for partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2003
Earlier work this paper cites.
Kernel density estimator in an infinite-dimensional space with a rate of convergence in the case of diffusion process
Sophie Dabo-Niang · 2004
Earlier work this paper cites.
Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2010
Earlier work this paper cites.
Nonparametric density estimation for functional data by delta sequences
BLS Prakasa Rao · 2010
Earlier work this paper cites.
The insar scientific computing environment
Paul A Rosen, Eric Gurrola, Gian Franco Sacco, and Howard Zebker · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Seismic wave propagation and inversion with neural operators
Yan Yang, Angela F Gao, Jorge C Castellanos, Zachary E Ross, Kamyar Azizzadenesheli, and Robert W Clayton · 2018
Later among the works it cites.
Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
Belinda Tzen and Maxim Raginsky · 2019
Later among the works it cites.
Generative models as distributions of functions
Emilien Dupont, Yee Whye Teh, and Arnaud Doucet · 2021
Later among the works it cites.
Neural sdes as infinite-dimensional gans
Patrick Kidger, James Foster, Xuechen Li, and Terry J Lyons · 2021
Later among the works it cites.
Neural operator: Learning maps between function spaces
Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
Cited alongside, same era.
Banach wasserstein gan
Jonas Adler and Sebastian Lunz · 2018
Cited alongside, same era.
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh · 2018
Cited alongside, same era.
Later among the works it cites.
Physics-informed neural operator for learning partial differential equations
Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, and Anima Anandkumar · 2021
Later among the works it cites.
The random feature model for input-output maps between banach spaces
Nicholas H Nelsen and Andrew M Stuart · 2021
Later among the works it cites.
U-fno–an enhanced fourier neural operator based-deep learning model for multiphase flow
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, and Sally M Benson · 2021
Later among the works it cites.
Wasserstein gans with gradient penalty compute congested transport
Tristan Milne and Adrian I Nachman · 2022
Closest in time.
U-no: U-shaped neural operators
Md Ashiqur Rahman, Zachary E Ross, and Kamyar Azizzadenesheli · 2022
Closest in time.