Paper Citation Record · LEDGER

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.02254.

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

pith.paper-citation-record.v1
2506.02254 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:32:34.868184Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact3
verified fuzzy21
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c75c7f65-84f5-47b1-a94e-ace332723d81 · outbound

This paper cites an unresolved cited work.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:32:40.663398Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 41f8c1ca-1bfd-49e4-8914-9cfe6174dc9a · outbound

This paper cites an unresolved cited work.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Unresolved cited work

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81080dd7-d490-479e-8ad4-aa0470133467 · outbound

This paper cites Generative Learning for Forecasting the Dynamics of Complex Systems.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Generative Learning for Forecasting the Dynamics of Complex Systems

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.777475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16940b0d-d843-42dc-ac49-216b89db3735 · outbound

This paper cites Generative Learning for Slow Manifolds and Bifurcation Diagrams.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Generative Learning for Slow Manifolds and Bifurcation Diagrams

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:32:35.835235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 374f2c34-fbd2-4085-b364-f1c586b8b93a · outbound

This paper cites Generative Learning of Densities on Manifolds.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Generative Learning of Densities on Manifolds

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:32:35.604978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc5402ed-53b1-4288-9355-dec3e4b84a2c · outbound

This paper cites Highly accurate protein structure prediction with alphafold.nature, 596(7873):583– 589, 2021.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Highly accurate protein structure prediction with alphafold.nature, 596(7873):583– 589, 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:40.471726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1dcf2a3-bf44-41a6-ad11-28a873ae3dcb · outbound

This paper cites Applicationsofgenerativeadversarialnetworksinneuroimaging and clinical neuroscience.Neuroimage, 269:119898, 2023.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Applicationsofgenerativeadversarialnetworksinneuroimaging and clinical neuroscience.Neuroimage, 269:119898, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:40.286580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e0fb0221-0824-465d-bf90-5f63c34f0ebf · outbound

This paper cites Learning particle physics by example: location-aware generative adversarial networks for physics synthesis.Computing and Software for Big Science, 1(1):4, 2017.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Learning particle physics by example: location-aware generative adversarial networks for physics synthesis.Computing and Software for Big Science, 1(1):4, 2017

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:40.073733Z

Source-reported events for the cited work

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Observation e226c4ac-9d37-474a-83d4-6286abb0f6f5 · outbound

This paper cites Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials.npj Computational Materials, 6(1):84, 2020.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials.npj Computational Materials, 6(1):84, 2020

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:39.853704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b455474b-26ac-41ec-8b6d-06090d28a619 · outbound

This paper cites Producing realistic climate data with generative adversarial networks.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Producing realistic climate data with generative adversarial networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:39.637058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7af8dc5a-f547-4ee9-8458-ae704e1164ee · outbound

This paper cites Auto-Encoding Variational Bayes.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Auto-Encoding Variational Bayes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.306039Z

Source-reported events for the cited work

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Observation 51b0eb3f-1fb4-4498-b2bf-830113d210a2 · outbound

This paper cites Stochastic backpropa- gation and approximate inference in deep generative models.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Stochastic backpropa- gation and approximate inference in deep generative models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:39.424373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 89b34c4e-6f42-4525-9fa4-51856e3921d2 · outbound

This paper cites The neural autoregressive distribution estimator.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps The neural autoregressive distribution estimator

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:39.222055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88faefdb-ee9a-4723-a8ab-8145fdee894a · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Normalizing flows for probabilistic modeling and inference

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.483651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17978e51-1938-4d5a-9de1-c57e82a4d998 · outbound

This paper cites Generative adversarial networks.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Generative adversarial networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.555700Z

Source-reported events for the cited work

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Observation 821e64c7-5db9-4b0f-98e3-31ec471857b8 · outbound

This paper cites Conditional Generative Adversarial Nets.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Conditional Generative Adversarial Nets

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.647603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec3f4837-a55c-4d45-9fdd-cef3317976c6 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Score-Based Generative Modeling through Stochastic Differential Equations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.722931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7454d13e-be29-47e8-b3d6-271def7e86d1 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems, 32, 2019.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems, 32, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.816237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a045dd3-9086-4132-81b6-9e269b955e02 · outbound

This paper cites Denoising diffusion probabilistic models.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Denoising diffusion probabilistic models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.871729Z

Source-reported events for the cited work

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Observation 82ac85e9-d91a-49b6-80f3-d16ddc2074b5 · outbound

This paper cites Improved techniques for training score-based generative models.Advances in neural information processing systems, 33:12438–12448, 2020.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Improved techniques for training score-based generative models.Advances in neural information processing systems, 33:12438–12448, 2020

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.947961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd3fe18c-bc27-4875-9cbb-b880c4716577 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:32.977818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69dce3da-b663-4b28-8aee-9003d563b975 · outbound

This paper cites Permutation invariant graph generation via score-based generative modeling.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Permutation invariant graph generation via score-based generative modeling

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:38.997884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f58c497-9fc6-44bf-a16e-121e8bd2f850 · outbound

This paper cites Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models.BioRxiv, pages 2022–12, 2022.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models.BioRxiv, pages 2022–12, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:38.822014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3512b2da-a8c3-4ba2-8ed6-56926c59afb3 · outbound

This paper cites Data-driven probability concentration and sampling on manifold.Journal of Computational Physics, 321:242–258, 2016.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Data-driven probability concentration and sampling on manifold.Journal of Computational Physics, 321:242–258, 2016

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:38.634214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c93e44e1-0522-402b-8c8c-c7b9d13a3130 · outbound

This paper cites Entropy-based closure for probabilistic learning on manifolds.Journal of Computational Physics, 388:518–533, 2019.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Entropy-based closure for probabilistic learning on manifolds.Journal of Computational Physics, 388:518–533, 2019

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:38.464082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d0aed6b-04dc-47ee-b819-6a0b63297b71 · outbound

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Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:32:38.282338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d85a5add-8426-412e-b863-52d3a1f24612 · outbound

This paper cites Probabilistic learning on manifolds (plom) with partition.International Journal for Numerical Methods in Engineering, 123(1):268–290, 2022.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Probabilistic learning on manifolds (plom) with partition.International Journal for Numerical Methods in Engineering, 123(1):268–290, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:38.139132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c216fc88-0a63-4e8e-a556-6d79959e117a · outbound

This paper cites Testing the manifold hypothesis.Journal of the American Mathematical Society, 29(4):983–1049, 2016.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Testing the manifold hypothesis.Journal of the American Mathematical Society, 29(4):983–1049, 2016

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:33.611866Z

Source-reported events for the cited work

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Observation 5fe95833-059e-43c4-9eb8-81062fbce7c2 · outbound

This paper cites The isomap algorithm and topological stability.Science, 295(5552):7–7, 2002.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps The isomap algorithm and topological stability.Science, 295(5552):7–7, 2002

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:37.993561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bea117e6-41fe-4729-8557-fe80b73066e7 · outbound

This paper cites Nonlinear dimensionality reduction by locally linear embedding.science, 290(5500):2323–2326, 2000.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Nonlinear dimensionality reduction by locally linear embedding.science, 290(5500):2323–2326, 2000

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:33.848728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aa8ff47-3abd-49d0-a3dd-bd2ca34a9041 · outbound

This paper cites Diffusionmaps.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Diffusionmaps

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:37.816836Z

Source-reported events for the cited work

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Observation a5e23520-6f23-437f-96ce-680bc4eddba3 · outbound

This paper cites Double diffusion maps and their latent harmonics for scientific computations in latent space.Journal of Computational Physics, 485:112072, 2023.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Double diffusion maps and their latent harmonics for scientific computations in latent space.Journal of Computational Physics, 485:112072, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:37.543658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation beb038f8-a9ce-4693-88da-8ce492b5f440 · outbound

This paper cites Geometric harmonics: a novel tool for multiscale out-of- sample extension of empirical functions.Applied and Computational Harmonic Analysis, 21(1):31–52, 2006.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Geometric harmonics: a novel tool for multiscale out-of- sample extension of empirical functions.Applied and Computational Harmonic Analysis, 21(1):31–52, 2006

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:37.325738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 87036e68-c10e-432c-931e-56826d5bb5f0 · outbound

This paper cites Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study.Applied and Computational Harmonic Analysis, 44(3):759–773, 2018.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study.Applied and Computational Harmonic Analysis, 44(3):759–773, 2018

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:37.148006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d0a75ae1-4d8b-4754-85e4-abab4f11923d · outbound

This paper cites Transient anisotropic kernel for probabilistic learning on manifolds.Computer Methods in Applied Mechanics and Engineering, 432 B, 2024.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Transient anisotropic kernel for probabilistic learning on manifolds.Computer Methods in Applied Mechanics and Engineering, 432 B, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:36.929879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df4bbf7a-1d76-4290-b8a0-5e96eddf74ed · outbound

This paper cites Sampling of bayesian posteriors with a non-gaussian probabilistic learning on manifolds from a small dataset.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Sampling of bayesian posteriors with a non-gaussian probabilistic learning on manifolds from a small dataset

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:36.649242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3ac0935d-557d-4341-b6fa-74efb3341a50 · outbound

This paper cites Physics-constrained non-gaussian probabilistic learning on manifolds.International Journal for Numerical Methods in Engineering, 121(1):110–145, 2020.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Physics-constrained non-gaussian probabilistic learning on manifolds.International Journal for Numerical Methods in Engineering, 121(1):110–145, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:36.474270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02d006ca-c535-4008-9e8f-105def387841 · outbound

This paper cites an unresolved cited work.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:32:36.284059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 82a3d7ed-2b66-49f2-b71b-c8cad7ac00e3 · outbound

This paper cites Probabilistic nonconvex constrained optimization with fixed number of function evaluations.International Journal for Numerical Methods in Engineering, 113(4):719–741, 2018.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Probabilistic nonconvex constrained optimization with fixed number of function evaluations.International Journal for Numerical Methods in Engineering, 113(4):719–741, 2018

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:36.114208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c248685-5c50-44ba-ae22-764cef6fc81d · outbound

This paper cites Uncertainty Quantification and Flow Dynamics in Rotating Detonation Engines.

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps Uncertainty Quantification and Flow Dynamics in Rotating Detonation Engines

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:32:35.242244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

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