Fetching the paper…
Reading the bibliography…
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences.
Equivariant differential topology
Arthur G Wasserman · 1969
Earlier work this paper cites.
Equivariant dynamical systems
MJ Field · 1980
Earlier work this paper cites.
Riemannian geometry / Manfredo do Carmo ; translated by Francis Flaherty
Manfredo Perdigão do Carmo · 1992
Earlier work this paper cites.
The Fokker-Planck Equation: Methods of Solution and Applications
Hannes Risken and Till Frank · 1996
Earlier work this paper cites.
Asteroid impact tsunami of 2880 March 16
Steven N Ward and Erik Asphaug · 2003
Earlier work this paper cites.
Lie groups
Daniel Bump · 2004
Earlier work this paper cites.
Eigenfunctions of the laplacian on compact riemannian manifolds
Harold G. Donnelly · 2006
Earlier work this paper cites.
Sampling realistic protein conformations using local structural bias
Thomas Hamelryck, John T Kent, and Anders Krogh · 2006
Earlier work this paper cites.
How to generate random matrices from the classical compact groups
Francesco Mezzadri · 2007
Earlier work this paper cites.
Earth impact database, 2011
Earth Impact Database · 2011
Earlier work this paper cites.
Rigid motion estimation using mixtures of projected gaussians
Wendelin Feiten, Muriel Lang, and Sandra Hirche · 2013
Earlier work this paper cites.
Introduction to Smooth Manifolds
John M Lee · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Meteorite landings dataset, March 2017
Meteorite Landings · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zach DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Cited alongside, same era.
Latent variable modelling with hyperbolic normalizing flows
Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, and Will Hamilton · 2020
Later among the works it cites.
Sampling using s u ( n ) 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 · 2020
Later among the works it cites.
Neural ordinary differential equations on manifolds
Luca Falorsi and Patrick Forré · 2020
Later among the works it cites.
Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
Later among the works it cites.
Differential Geometry and Lie Groups: A Computational Perspective , volume 12
Jean Gallier and Jocelyn Quaintance · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Taco S. Cohen, Mario Geiger, Jonas Köhler, and Max Welling · 2018
Cited alongside, same era.
On the generalization of equivariance and convolution in neural networks to the action of compact groups
Risi Kondor and Shubhendu Trivedi · 2018
Cited alongside, same era.
Gauge equivariant convolutional networks and the icosahedral CNN
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling · 2019
Cited alongside, same era.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Cited alongside, same era.
Scalable reversible generative models with free-form continuous dynamics
Will Grathwohl, Ricky T Q Chen, Jesse Bettencourt, and David Duvenaud · 2019
Cited alongside, same era.
A wrapped normal distribution on hyperbolic space for gradient-based learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, and Masanori Koyama · 2019
Cited alongside, same era.
Later among the works it cites.
Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
Later among the works it cites.
Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noe · 2020
Later among the works it cites.
Neural manifold ordinary differential equations
Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa · 2020
Later among the works it cites.
Riemannian continuous normalizing flows
Emile Mathieu and Maximilian Nickel · 2020
Later among the works it cites.
Normalizing flows on tori and spheres
Danilo Jimenez Rezende, George Papamakarios, Sebastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer · 2020
Later among the works it cites.
Mixed-curvature variational autoencoders
Ondrej Skopek, Octavian-Eugen Ganea, and Gary Bécigneul · 2020
Later among the works it cites.
Targeted free energy estimation via learned mappings
Peter Wirnsberger, Andrew J Ballard, George Papamakarios, Stuart Abercrombie, Sébastien Racanière, Alexander Pritzel, Danilo Jimenez Rezende, and Charles Blundell · 2020
Later among the works it cites.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velivcković · 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.