Paper Citation Record · LEDGER

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2504.19779.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.19779 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T23:45:45.765289Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T23:50:47.745507Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation ad4bba90-accb-4813-88ab-ae9940dee30a · outbound

This paper cites Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models,

Reference 1

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This paper cites A comprehensive survey and analysis of generative models in machine learning,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks A comprehensive survey and analysis of generative models in machine learning,

Reference 2

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Observation 94f80096-1e2c-40b3-bce4-c9d17cf13388 · outbound

This paper cites Refinements of universal approximation results for deep belief networks and restricted boltzmann machines,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Refinements of universal approximation results for deep belief networks and restricted boltzmann machines,

Reference 3

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This paper cites Deep Boltzmann machines,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Deep Boltzmann machines,

Reference 4

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Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Implicit generation and modeling with energy based models,

Reference 5

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Observation 17858f02-e47b-4026-9efc-981023798c89 · outbound

This paper cites Density estimation using Real NVP,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Density estimation using Real NVP,

Reference 6

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Observation 99a8b7c5-cd76-432e-a0b7-004991c6ad44 · outbound

This paper cites Normalizing flows for probabilistic modeling and inference,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Normalizing flows for probabilistic modeling and inference,

Reference 7

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This paper cites Coupling-based invertible neu- ral networks are universal diffeomorphism approximators,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Coupling-based invertible neu- ral networks are universal diffeomorphism approximators,

Reference 8

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This paper cites LU-Net: Invertible neural networks based on matrix factor- ization,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks LU-Net: Invertible neural networks based on matrix factor- ization,

Reference 9

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This paper cites Generative adversarial networks,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Generative adversarial networks,

Reference 10

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Observation cfca391d-9921-4366-891b-ed18223c6dd9 · outbound

This paper cites f-gan: Training generative neural samplers using variational divergence minimization,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks f-gan: Training generative neural samplers using variational divergence minimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:53:32.212706Z

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This paper cites Temporal shift GAN for large scale video generation,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Temporal shift GAN for large scale video generation,

Reference 12

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This paper cites ESRGAN: Enhanced super-resolution generative adversarial networks,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks ESRGAN: Enhanced super-resolution generative adversarial networks,

Reference 13

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Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Neural ordinary differential equations,

Reference 14

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Observation e8d721dd-2d6f-4e5c-87fb-87be25f75e2f · outbound

This paper cites Flow matching for generative modeling,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Flow matching for generative modeling,

Reference 15

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Observation ef30c046-e947-4544-9ddb-4404034e912a · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Flow straight and fast: Learning to generate and transfer data with rectified flow,

Reference 16

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 17

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Observation f7d9bb0e-79cc-427c-a958-0c294a1846b4 · outbound

This paper cites Bayesian learning via stochastic gradient Langevin dynamics,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Bayesian learning via stochastic gradient Langevin dynamics,

Reference 18

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Observation f4ea1aaf-82d7-4667-8b1a-075f3021a8d0 · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks High-resolution image synthesis with latent diffusion models,

Reference 19

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Observation a1f250f2-eb87-4043-902c-fe1ec72022f3 · outbound

This paper cites Polar factorization and monotone rearrangement of vector-valued functions,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Polar factorization and monotone rearrangement of vector-valued functions,

Reference 20

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Observation 6e2cc373-c2e8-4382-b912-c6b9f7fcf732 · outbound

This paper cites Regularity as regularization: Smooth and strongly con- vex Brenier potentials in optimal transport,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Regularity as regularization: Smooth and strongly con- vex Brenier potentials in optimal transport,

Reference 21

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Observation 848dc2d5-c0d8-4940-bc30-507ac97304c2 · outbound

This paper cites Santambrogio,Optimal Transport for Applied Mathematicians, vol.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Santambrogio,Optimal Transport for Applied Mathematicians, vol

Reference 22

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Observation c5ff1848-6322-4296-8389-6cecb4a803ee · outbound

This paper cites A Convenient Infinite Dimensional Framework for Generative Adversarial Learning.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks A Convenient Infinite Dimensional Framework for Generative Adversarial Learning

Reference 23

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This paper cites Some theoretical properties of GANs,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Some theoretical properties of GANs,

Reference 24

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Observation 384c013a-008e-4e12-83f1-6086fbbcc860 · outbound

This paper cites Rates of convergence for density estimation with generative adversarial networks,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Rates of convergence for density estimation with generative adversarial networks,

Reference 25

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This paper cites Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations,

Reference 26

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Observation 8de3131e-fae5-4bd2-8e66-ee58ab65ae15 · outbound

This paper cites Error bounds for approximations with deep relu networks,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Error bounds for approximations with deep relu networks,

Reference 27

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Observation 7bf1d062-3279-4242-af95-be60f7d8daec · outbound

This paper cites Input convex neural networks,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Input convex neural networks,

Reference 28

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Observation f224b0e7-30d4-4510-b408-19291863a05e · outbound

This paper cites Convex potential flows: Universal probability distributions with optimal transport and convex optimization,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Convex potential flows: Universal probability distributions with optimal transport and convex optimization,

Reference 29

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Observation 84ab130b-9668-49f6-a5c8-d687c23b2758 · outbound

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Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Wasserstein2 generative networks,

Reference 30

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Observation 7c21a56f-8be7-4c75-b0cd-243aa16e4faf · outbound

This paper cites Optimal control via neural networks: A convex approach,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Optimal control via neural networks: A convex approach,

Reference 31

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Observation fe213aa2-ea30-479e-b0e0-75a63684b000 · outbound

This paper cites Input Convex Gradient Networks.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Input Convex Gradient Networks

Reference 32

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Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Gradient Networks

Reference 33

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Observation 38dc4268-6222-400d-a795-577a5de74a42 · outbound

This paper cites Rates of convergence for density estimation with generative adversarial networks.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Rates of convergence for density estimation with generative adversarial networks

Reference 34

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Observation 0e24bfcf-ec39-4e19-abbd-a604929d4d74 · outbound

This paper cites Schumaker,Spline Functions: Basic Theory.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Schumaker,Spline Functions: Basic Theory

Reference 35

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Observation bb78387f-c64d-4f93-a1cf-c545dd35dc3c · outbound

This paper cites Villaniet al., Optimal Transport: Old and New, vol.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Villaniet al., Optimal Transport: Old and New, vol

Reference 36

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Observation e6a53f17-8a78-4f03-be5b-0d908c050f0e · outbound

This paper cites Boundary regularity of maps with convex potentials – II,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Boundary regularity of maps with convex potentials – II,

Reference 37

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Learning Brenier Potentials with Convex Generative Adversarial Neural Networks On the volume of tubes,

Reference 38

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This paper cites Gray,Tubes, vol.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Gray,Tubes, vol

Reference 39

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Observation a4fce207-098d-4aa0-833b-5e81fbb3427e · outbound

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Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Unresolved cited work

Reference 40

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Observation 753d9a4e-bbbc-49a2-b09a-a766a147c52a · outbound

This paper cites The MNIST database of handwritten digits,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks The MNIST database of handwritten digits,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:53:30.722606Z

Source-reported events for the cited work

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Observation 5eecdb7a-beec-4628-9607-e84ff9dae5eb · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T05:53:30.417638Z

Source-reported events for the cited work

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Observation ddf1716d-e882-4886-a7c3-99e775245351 · outbound

This paper cites Learning methods for generic object recognition with invariance to pose and lighting,.

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks Learning methods for generic object recognition with invariance to pose and lighting,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:53:30.634437Z

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Pith citing papers

Observation 904752f9-6a30-40dd-95e5-3798ea6ca5f6 · inbound

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs cites this paper.

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs Learning Brenier Potentials with Convex Generative Adversarial Neural Networks

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.