Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels | Paper Citation Record

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2026 , eprint=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Weighted minimum $\alpha$-Green energy problems

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This paper cites On the role of the point at infinity in Deny's principle of positivity of mass for Riesz potentials.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels On the role of the point at infinity in Deny's principle of positivity of mass for Riesz potentials

Reference 3

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Riesz energy problems with external fields and related theory

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This paper cites Ten equivalent definitions of the fractional Laplace operator.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Ten equivalent definitions of the fractional Laplace operator

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Wasserstein gradient flows for Coulomb discrepancies

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This paper cites doi:10.1007/BFb0103945 , isbn =.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels doi:10.1007/BFb0103945 , isbn =

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This paper cites Sharp Rates of MMD Empirical Estimation with Power Kernels.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Sharp Rates of MMD Empirical Estimation with Power Kernels

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This paper cites International Conference on Learning Representations , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels International Conference on Learning Representations , volume=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2015 , eprint=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels On Kato-Ponce and fractional Leibniz

Reference 13

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This paper cites ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels ESAIM Control Optim

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This paper cites Conference on learning theory , pages=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Conference on learning theory , pages=

Reference 18

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This paper cites Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach

Reference 19

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This paper cites Stochastic Processes and their Applications , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Stochastic Processes and their Applications , volume=

Reference 20

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels NeurIPS , year=

Reference 21

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This paper cites Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective

Reference 22

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Reference 23

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 4: applications of harmonic analysis , author=

Reference 24

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2019 , publisher=

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This paper cites Equivalence of gradient flows and entropy solutions for singular nonlocal interaction equations in.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Equivalence of gradient flows and entropy solutions for singular nonlocal interaction equations in

Reference 26

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This paper cites Wasserstein steepest descent flows of discrepancies with.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Wasserstein steepest descent flows of discrepancies with

Reference 27

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Generalization of an inequality by

Reference 28

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2008 , publisher=

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This paper cites Communications on Pure and Applied Mathematics , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Communications on Pure and Applied Mathematics , volume=

Reference 30

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Communications in Partial Differential Equations , volume=

Reference 31

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels European Congress of Mathematics , volume=

Reference 32

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Sharp conditions to avoid collisions in singular

Reference 33

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Uniqueness of the solution to the

Reference 34

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Communications on pure and applied mathematics , volume=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels A computational fluid mechanics solution to the

Reference 36

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Acta Mathematica , volume=

Reference 37

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Advances in mathematics , volume=

Reference 38

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Finite-time blow-up of

Reference 39

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Classical

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels The Journal of Machine Learning Research , volume=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels On the global convergence of

Reference 42

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Journal of Differential Equations , volume=

Reference 43

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Characterization of translation invariant

Reference 44

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2021 , publisher=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels SIAM Journal on Mathematical Analysis , author=

Reference 46

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Lectures on Coulomb and Riesz gases

Reference 47

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Sharp commutator estimates of all order for

Reference 48

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Mean field limit for

Reference 49

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Mean-field limits of

Reference 51

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Hitchhiker's guide to the fractional

Reference 52

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Sharp constants in the

Reference 53

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Observation 2525c7fb-34fa-4e07-8599-95e4eab06b9a · outbound

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2001 , publisher=

Reference 54

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels On the Best Values of the Constants in the Theorems of

Reference 55

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels , author=

Reference 56

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This paper cites 1970 , publisher=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 1970 , publisher=

Reference 57

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This paper cites Sharp inequalities for martingales with applications to the.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Sharp inequalities for martingales with applications to the

Reference 58

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Observation 843192ea-3b06-4ae7-ab8c-4c15b2d71041 · outbound

This paper cites The variational formulation of the.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels The variational formulation of the

Reference 59

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This paper cites Advances in neural information processing systems , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Advances in neural information processing systems , volume=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Advances in neural information processing systems , volume=

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Advances in neural information processing systems , volume=

Reference 63

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Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Sampling as optimization in the space of measures: The

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.503164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 22a0c254-cbe1-43f7-8669-34720fe8e5e4 · outbound

This paper cites an unresolved cited work.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.500940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03fd9638-f1ac-4ea2-9a8c-e731a6693c60 · outbound

This paper cites Accelerating.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Accelerating

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.486230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c223af8f-08fa-4622-b7c9-c0f64752823e · outbound

This paper cites Variational inference via.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Variational inference via

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.475699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 01e7a443-7d7f-4e26-ba21-4ad257422a35 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Advances in Neural Information Processing Systems , volume=

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.464949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b59e04b9-f769-42b8-a675-8e8d995a23d9 · outbound

This paper cites an unresolved cited work.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.513175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46c5bb06-629e-4037-aa60-6f8728878033 · outbound

This paper cites Interacting.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Interacting

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.516549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10487580-0100-4acf-86ef-c851dca24484 · outbound

This paper cites Communications in applied mathematics and computational science , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Communications in applied mathematics and computational science , volume=

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.520873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df303e9f-4afc-47de-9e9f-9f400c49ef83 · outbound

This paper cites Nonlinearity , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Nonlinearity , volume=

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.431563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7df8f5c8-5899-4266-a5ce-b2b6a4ec2532 · outbound

This paper cites Ensemble.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Ensemble

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.419362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b948a375-9a48-4d46-acad-f1f380290e57 · outbound

This paper cites Studies in Applied Mathematics , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Studies in Applied Mathematics , volume=

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.533241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63617ceb-0b88-4443-939f-39832f3cd965 · outbound

This paper cites Communications in Mathematical Sciences , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Communications in Mathematical Sciences , volume=

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.402200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13cc1c06-da9b-4040-b721-4d8b9ed32dfb · outbound

This paper cites Advances in applied probability , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Advances in applied probability , volume=

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.541714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1fe40ee-ba85-44bd-adc8-a2a3132f37f1 · outbound

This paper cites Regularized.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Regularized

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.384520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b3de983d-c0ae-4382-b0e7-fe8f819fd618 · outbound

This paper cites an unresolved cited work.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:22:27.372748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5d77050b-c062-4d30-a6da-ced3a167a170 · outbound

This paper cites Proceedings of the National Conference on Artificial Intelligence , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Proceedings of the National Conference on Artificial Intelligence , volume=

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.362718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 706b80b4-c10a-42e5-90f6-2376a2b91f00 · outbound

This paper cites Journal of Machine Learning Research , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Journal of Machine Learning Research , volume=

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.352039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 86704deb-ed7e-4d44-a82c-7a195eff5060 · outbound

This paper cites Bernoulli , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Bernoulli , volume=

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.341902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83c46bc7-c98a-4d27-b198-0a51ba447dc3 · outbound

This paper cites Universality, Characteristic Kernels and.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Universality, Characteristic Kernels and

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.330585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4e667276-bf03-4c9e-bd60-e54b60100613 · outbound

This paper cites 2008 , publisher=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels 2008 , publisher=

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.568722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e955b136-6e3e-4975-880b-012c1b53e648 · outbound

This paper cites (De)-regularized.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels (De)-regularized

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.311397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 36f93bd9-4107-4b23-9600-4388da356481 · outbound

This paper cites Physica D: Nonlinear Phenomena , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Physica D: Nonlinear Phenomena , volume=

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.299283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7cdfdd4e-187b-4b6e-8622-f13de93b42e0 · outbound

This paper cites an unresolved cited work.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:22:27.287633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 21de0ce5-6db5-4424-a23c-975f50801e6f · outbound

This paper cites Posterior Sampling Based on Gradient Flows of the.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Posterior Sampling Based on Gradient Flows of the

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.275802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0d942c80-26cf-4948-b0d1-2bcc0a7a4156 · outbound

This paper cites Generative Sliced.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Generative Sliced

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.263661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation adaa4a62-e97a-4c36-9a95-6da7d9d47df9 · outbound

This paper cites Smoothed distance kernels for.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Smoothed distance kernels for

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.252384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 35cc554c-3adf-4859-b45c-e9b744dec11e · outbound

This paper cites Journal of Multivariate Analysis , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Journal of Multivariate Analysis , volume=

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.595821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88c29df9-0237-46bb-a492-b5ba765f0d47 · outbound

This paper cites Bowling Green State University, Department of Mathematics and Statistics Technical Report , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Bowling Green State University, Department of Mathematics and Statistics Technical Report , volume=

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.233232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2be34198-79c4-40aa-9d0e-11deccc39e46 · outbound

This paper cites Journal of statistical planning and inference , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Journal of statistical planning and inference , volume=

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.602896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0034b72f-753a-4b69-8e05-95a1e50bc66c · outbound

This paper cites Journal of Machine Learning Research , volume=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Journal of Machine Learning Research , volume=

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.606410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d0fa61e-cfc6-41ea-bb23-c067bb9f1931 · outbound

This paper cites Global-in-time mean-field convergence for singular.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Global-in-time mean-field convergence for singular

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.205751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 60d53c4e-dc51-4a27-8cab-f3b106ed2183 · outbound

This paper cites Commutators, mean-field, and supercritical mean-field limits for.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Commutators, mean-field, and supercritical mean-field limits for

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.192714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1c3481e-3346-448f-8d35-8175d377b0bc · outbound

This paper cites Wasserstein distances for vortices approximation of.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Wasserstein distances for vortices approximation of

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.181438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3dbe5353-9495-41be-9419-e6ee3c4126f5 · outbound

This paper cites Ecole d'.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Ecole d'

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.170695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6ab86f1f-8a06-4fe8-a559-ea76b97e3666 · outbound

This paper cites Quantitative estimates of propagation of chaos for stochastic systems with.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Quantitative estimates of propagation of chaos for stochastic systems with

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T15:22:26.624331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa163f18-5b4d-4510-99ab-e661eeeacd95 · outbound

This paper cites Active Particles, Volume 4: Theory, Models, Applications , pages=.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Active Particles, Volume 4: Theory, Models, Applications , pages=

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:22:27.152648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5b449f2a-8411-43c3-9e40-4f1a26931a29 · outbound

This paper cites an unresolved cited work.

Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:22:27.141409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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