Paper Citation Record · LEDGER

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds

As of 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2608.04827.

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

pith.paper-citation-record.v1
2608.04827 v1

Coverage vector

measured 43 of 43 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 1bd5181e-9e62-4849-ab06-53f0c63101a9 · outbound

This paper cites Denoising diffusion probabilistic models,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Denoising diffusion probabilistic models,

Reference 1

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This paper cites Generative modeling by esti- mating gradients of the data distribution,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Generative modeling by esti- mating gradients of the data distribution,

Reference 2

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Observation 50259c97-d89a-4747-a164-5cd36b5abd06 · outbound

This paper cites Elucidating the design space of diffusion-based generative models,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Elucidating the design space of diffusion-based generative models,

Reference 3

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This paper cites Text-to-video generation,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Text-to-video generation,

Reference 4

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Observation 1b2d9a7e-5802-4dcf-a132-1fd9b83ccdd0 · outbound

This paper cites A connection between score matching and denoising autoencoders,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds A connection between score matching and denoising autoencoders,

Reference 5

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Observation b8a79a54-1d32-4d00-8774-aee6fd277951 · outbound

This paper cites Deep unsupervised learning using nonequi- librium thermodynamics,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Deep unsupervised learning using nonequi- librium thermodynamics,

Reference 6

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Observation 29f87a21-080b-4c64-a2d0-e2d55b40ea82 · outbound

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

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Score-Based Generative Modeling through Stochastic Differential Equations

Reference 7

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This paper cites Test- ing the manifold hypothesis,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Test- ing the manifold hypothesis,

Reference 8

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This paper cites Extrinsic gaussian processes for regression and classification on manifolds,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Extrinsic gaussian processes for regression and classification on manifolds,

Reference 9

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This paper cites Riemannian diffusion models,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Riemannian diffusion models,

Reference 10

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This paper cites Riemannian diffusion models,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Riemannian diffusion models,

Reference 11

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Observation 34f2311b-9a7e-4404-b3ca-dd09cd051d80 · outbound

This paper cites Generative modeling on manifolds through mixture of riemannian diffusion processes,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Generative modeling on manifolds through mixture of riemannian diffusion processes,

Reference 12

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Observation 8c98af25-4e20-44ae-bbb2-3335eb7b7c40 · outbound

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

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds High-resolution image synthesis with latent diffusion models,

Reference 13

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This paper cites An introduction to variational autoencoders,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds An introduction to variational autoencoders,

Reference 14

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Observation cb9c026b-bc17-467b-8e93-f32fc62ecace · outbound

This paper cites Probabilistic non-linear principal com- ponent analysis with gaussian process latent variable models,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Probabilistic non-linear principal com- ponent analysis with gaussian process latent variable models,

Reference 15

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Observation ef02b1fe-2784-41a4-8619-db40acde7c90 · outbound

This paper cites Learning for larger datasets with the gaussian process latent variable model,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Learning for larger datasets with the gaussian process latent variable model,

Reference 16

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Observation 09d798b2-0588-423f-98bc-48f41798558c · outbound

This paper cites Revis- 10 iting active sets for gaussian process decoders,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Revis- 10 iting active sets for gaussian process decoders,

Reference 17

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Observation 6d7adf95-0dfc-4e23-b362-511be8252cc7 · outbound

This paper cites Columbia object image library (coil-100),.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Columbia object image library (coil-100),

Reference 18

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Observation fe758a58-7b8b-4260-8d7a-a50bb917308d · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?

Reference 19

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This paper cites Multi- centre, multi-vendor and multi-disease cardiac image segmentation challenge,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Multi- centre, multi-vendor and multi-disease cardiac image segmentation challenge,

Reference 20

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Gradient-based learning applied to document recogni- tion,

Reference 21

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Metrics for Probabilistic Geometries

Reference 22

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Fast and robust shortest paths on manifolds learned from data,

Reference 23

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Laplacian eigenmaps for dimensionality reduction and data representation,

Reference 24

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This paper cites Nonlinear dimensionality reduction by locally linear embedding,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Nonlinear dimensionality reduction by locally linear embedding,

Reference 25

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Unresolved cited work

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Unresolved cited work

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds The non-central wishart distribution and certain problems of multivariate statistics,

Reference 28

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Brownian motion and Riemannian geometry,

Reference 29

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds A brief introduction to Brownian motion on a Riemannian manifold,

Reference 30

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Higher-order implicit strong numerical schemes for stochastic differential equa- tions,

Reference 31

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Lamberton and B

Reference 32

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Estimation of non- normalized statistical models by score matching

Reference 33

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds U- net: Convolutional networks for biomedical image segmentation,

Reference 34

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Riemann manifold langevin and hamiltonian monte carlo methods,

Reference 35

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Stochastic gradient hamiltonian monte carlo,

Reference 36

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Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Amari and H

Reference 37

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Observation 424af5bd-59f1-4a50-8bee-550872567453 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Classifier-Free Diffusion Guidance

Reference 38

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This paper cites Diffusion models beat gans on image synthesis,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Diffusion models beat gans on image synthesis,

Reference 39

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This paper cites Progressive distillation for fast sampling of diffusion models,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Progressive distillation for fast sampling of diffusion models,

Reference 40

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Observation 3ce11394-b2e8-472c-af38-3834f1ca2afa · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 41

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This paper cites The unreasonable effectiveness of deep fea- tures as a perceptual metric,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds The unreasonable effectiveness of deep fea- tures as a perceptual metric,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:01.745551Z

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Observation f13e9be8-9d12-40f4-b59c-b2b319871384 · outbound

This paper cites The intrinsic dimension of images and its impact on learning,.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds The intrinsic dimension of images and its impact on learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:39:01.623697Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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