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Paper Citation Record · LEDGER

Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:1905.09883.

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pith.paper-citation-record.v1
1905.09883 v2

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measured 0 of 0 reference resolution

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measured 27 of 27 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:48:28.903824Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:47:59.658444Z

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Outbound references

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Pith citing papers

Observation e9904ae0-c033-4d48-9806-00fd13477f85 · inbound

Progressive Distillation for Fast Sampling of Diffusion Models cites this paper.

Progressive Distillation for Fast Sampling of Diffusion Models Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 19

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arxiv_id, observed 2026-05-11T09:37:44.738080Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation cfb89288-37a5-4c69-a26c-27ed75a37a64 · inbound

Video Diffusion Models cites this paper.

Video Diffusion Models Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 53

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arxiv_id, observed 2026-05-13T14:38:28.031814Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation dfa2729e-b3b1-41c6-8bc2-98f385c57f76 · inbound

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding cites this paper.

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 70

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arxiv_id, observed 2026-05-12T07:38:53.635651Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation f74010f5-b25d-40a2-8f4d-78869733980d · inbound

Imagen Video: High Definition Video Generation with Diffusion Models cites this paper.

Imagen Video: High Definition Video Generation with Diffusion Models Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 18

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arxiv_id, observed 2026-05-11T03:31:08.327186Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 208f4c38-333e-41bf-afa1-727a8dea55a0 · inbound

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation cites this paper.

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 78

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arxiv_id, observed 2026-05-13T20:06:44.662299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation bd897f67-f8fd-461a-97bd-b377b5f36daa · inbound

Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations cites this paper.

Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 79

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arxiv_id, observed 2026-05-22T19:15:03.501215Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 22d51574-4f01-47a7-9a17-38a590199344 · inbound

Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions cites this paper.

Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 43

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arxiv_id, observed 2026-05-22T00:14:27.736471Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 12ec08fb-4f7d-4449-a01c-599bf99cb9dd · inbound

Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments cites this paper.

Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 26

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Observation 4d2838fc-e670-4790-b5fb-f436ff28b167 · inbound

Numerical PDE solvers outperform neural PDE solvers cites this paper.

Numerical PDE solvers outperform neural PDE solvers Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 2022

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no resolver link, observed 2026-08-06T13:03:53.945985Z

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Observation 2b25a571-9682-4d5d-bf7f-1a12df3c4602 · inbound

Data-to-Energy Stochastic Dynamics cites this paper.

Data-to-Energy Stochastic Dynamics Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 9

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no resolver link, observed 2026-08-04T13:40:54.638046Z

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Observation 599229ed-edd4-472a-98dc-06e1d2402751 · inbound

Deep Neural Networks Inspired by Differential Equations cites this paper.

Deep Neural Networks Inspired by Differential Equations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 242

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no resolver link, observed 2026-08-04T10:54:40.952765Z

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Observation 0caad7c5-2235-43ed-8ce3-73d2ed9c0c0f · inbound

DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations cites this paper.

DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 39

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arxiv_id, observed 2026-05-16T21:08:33.094603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation efc5d61f-aaf2-4a73-beec-4efbc8809510 · inbound

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations cites this paper.

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 15

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arxiv_id, observed 2026-05-16T09:40:48.815949Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 559754d5-67ef-4519-8649-7d3afb098df6 · inbound

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations cites this paper.

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 2002

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Unavailable: canonical work link unavailable.

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Observation d4094f8d-098c-467c-83f8-88a082537e8e · inbound

MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data cites this paper.

MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 64

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arxiv_id, observed 2026-05-15T00:18:21.898642Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation b67d893a-6212-4be0-b14e-6857bbdd84c1 · inbound

Neural Stochastic Processes for Satellite Precipitation Refinement cites this paper.

Neural Stochastic Processes for Satellite Precipitation Refinement Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 63

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arxiv_id, observed 2026-05-11T09:05:58.651365Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Finite Expression Method with TranNet-based Function Learning for High-Dimensional Partial Differential Equations cites this paper.

Finite Expression Method with TranNet-based Function Learning for High-Dimensional Partial Differential Equations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 56

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arxiv_id, observed 2026-05-11T19:51:11.185867Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows cites this paper.

Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 32

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arxiv_id, observed 2026-05-11T21:21:12.027789Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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The finite expression method for turbulent dynamics with high-order moment recovery cites this paper.

The finite expression method for turbulent dynamics with high-order moment recovery Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 43

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 00b1b88b-499a-4882-b8ae-7e26460bb40a · inbound

Variational Inference for L\'evy Process-Driven SDEs via Neural Tilting cites this paper.

Variational Inference for L\'evy Process-Driven SDEs via Neural Tilting Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 55

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arxiv_id, observed 2026-05-12T07:16:30.102041Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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The Transformer as a Polar State Estimator cites this paper.

The Transformer as a Polar State Estimator Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 152

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arxiv_id, observed 2026-05-13T02:07:09.259117Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation ccfe1543-7064-4b42-abfa-d868eb8e33d1 · inbound

Towards Continuous-time Causal Foundation Models cites this paper.

Towards Continuous-time Causal Foundation Models Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 2

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arxiv_id, observed 2026-06-29T19:53:55.474798Z

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Observation 9f58cd0c-69cb-4221-8ab8-d208f295874f · inbound

Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations cites this paper.

Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 29

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Observation 80b6f1d3-edaa-4b64-b027-7ae2443f821f · inbound

Policy Gradient for Continuous-Time Robust Markov Decision Processes cites this paper.

Policy Gradient for Continuous-Time Robust Markov Decision Processes Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 32

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arxiv_id, observed 2026-07-02T06:56:44.470334Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 0bb9cd10-1b54-43a4-a299-9ab9101417cf · inbound

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming cites this paper.

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 68

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arxiv_id, observed 2026-07-02T12:56:56.958722Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation c6759e90-5969-4722-a70b-253acf23f3e1 · inbound

First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems cites this paper.

First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 62

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 2a79af26-bf98-455d-93d9-fdbb81447b3a · inbound

What Uncertainties Do We Need for Dynamical Systems? cites this paper.

What Uncertainties Do We Need for Dynamical Systems? Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 13

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arxiv_id, observed 2026-07-03T09:47:59.659895Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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