Fetching the paper…
Reading the bibliography…
The gradients of convex functions are expressive models of non-trivial vector fields.
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun · 1909
Earlier work this paper cites.
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 1909
Earlier work this paper cites.
On certain locally flat homogeneous manifolds of solvable Lie groups
Hirohiko Shima · 1976
Earlier work this paper cites.
Polar factorization and monotone rearrangement of vector-valued functions
Yann Brenier · 1991
Earlier work this paper cites.
Topics in Matrix Analysis
R.A. Horn and C.R. Johnson · 1994
Earlier work this paper cites.
The variational formulation of the fokker–planck equation
Richard Jordan, David Kinderlehrer, and Felix Otto · 1998
Earlier work this paper cites.
Topics in Optimal Transportation
C. Villani · 2003
Earlier work this paper cites.
Evaluating derivatives: principles and techniques of algorithmic differentiation
Andreas Griewank and Andrea Walther · 2008
Earlier work this paper cites.
Barycenters in the wasserstein space
Martial Agueh and Guillaume Carlier · 2011
Earlier work this paper cites.
Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, and Aaron C. Courville · 2012
Earlier work this paper cites.
Chin-Wei Huang, Ricky TQ Chen, Christos Tsirigotis, and Aaron Courville · 2012
Earlier work this paper cites.
Introduction to Smooth Manifolds
J.M. Lee · 2013
Cited alongside, same era.
Learning generative models with sinkhorn divergences, 2017
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2017
Cited alongside, same era.
Wasserstein gan, 2017
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Improved training of wasserstein gans, 2017
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
Cited alongside, same era.
Input convex neural networks
Brandon Amos, Lei Xu, and J Zico Kolter · 2017
Cited alongside, same era.
Sobolev training for neural networks
Wojciech Marian Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Świrszcz, and Razvan Pascanu · 2017
Cited alongside, same era.
On approximating ∇ f \nabla f with neural networks
Saeed Saremi · 2019
Later among the works it cites.
Sorting out lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2019
Later among the works it cites.
Jacnet: Learning functions with structured jacobians
Jonathan Lorraine and Safwan Hossain · 2019
Later among the works it cites.
Introduction to Riemannian Manifolds
J.M. Lee · 2019
Later among the works it cites.
Optimal transport mapping via input convex neural networks
Ashok Makkuva, Amirhossein Taghvaei, Sewoong Oh, and Jason Lee · 2020
Later among the works it cites.
An inductive bias for distances: Neural nets that respect the triangle inequality
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Improving gans using optimal transport, 2018
Tim Salimans, Han Zhang, Alec Radford, and Dimitris Metaxas · 2018
Cited alongside, same era.
Primal-dual wasserstein gan, 2018
Mevlana Gemici, Zeynep Akata, and Max Welling · 2018
Cited alongside, same era.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Cited alongside, same era.
Neural autoregressive flows, 2018
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
Cited alongside, same era.
Wasserstein-2 generative networks
Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, and Evgeny Burnaev · 2019
Cited alongside, same era.
Silviu Pitis, Harris Chan, Kiarash Jamali, and Jimmy Ba · 2020
Later among the works it cites.
Neural spatio-temporal point processes
Ricky TQ Chen, Brandon Amos, and Maximilian Nickel · 2020
Later among the works it cites.
Gradients are not all you need
Luke Metz, C Daniel Freeman, Samuel S Schoenholz, and Tal Kachman · 2021
Closest in time.
Optimizing functionals on the space of probabilities with input convex neural networks
David Alvarez-Melis, Yair Schiff, and Youssef Mroueh · 2021
Closest in time.
Large-scale wasserstein gradient flows, 2021
Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, and Evgeny Burnaev · 2021
Closest in time.
Scalable computations of wasserstein barycenter via input convex neural networks, 2021
Jiaojiao Fan, Amirhossein Taghvaei, and Yongxin Chen · 2021
Closest in time.