Paper Citation Record · LEDGER

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

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 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 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:45:54.937341Z

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

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Pith citing papers

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Normalizing Flows: An Introduction and Review of Current Methods cites this paper.

Normalizing Flows: An Introduction and Review of Current Methods Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 24

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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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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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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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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

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:31:08.327186Z

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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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Observation f11ec8c7-726c-4e32-8a35-aa891a707ee5 · inbound

Deep Learning-based Approaches for State Space Models: A Selective Review cites this paper.

Deep Learning-based Approaches for State Space Models: A Selective Review Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 149

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Observation 59793944-28f2-4022-9e6d-b3c996ce33fd · inbound

Improving the Noise Estimation of Latent Neural Stochastic Differential Equations cites this paper.

Improving the Noise Estimation of Latent Neural Stochastic Differential Equations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 23

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Observation 8dfb7928-c9c8-4a76-8919-e09dc8daa0b6 · inbound

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations cites this paper.

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 57

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Observation 3625bbed-c4f7-46f0-a2c7-b839380e875b · inbound

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models cites this paper.

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 7

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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

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:15:03.501215Z

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Observation e1822eb5-70df-455c-9c07-ea809901c84a · inbound

Uncertainty quantification of neural network models of evolving processes via Langevin sampling cites this paper.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 20

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Observation e2926fce-f8db-417d-ae70-b6d831b30252 · inbound

Efficient Training of Neural SDEs Using Stochastic Optimal Control cites this paper.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 1

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Observation 2a612cd0-a6bf-4e37-b86d-61e22bc43da2 · inbound

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis cites this paper.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 22

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Observation 51863b31-1df0-4fd6-9532-bf71640235da · inbound

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data cites this paper.

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 3

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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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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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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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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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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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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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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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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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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

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:05:58.651365Z

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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

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:51:11.185867Z

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Observation fe82af8d-657e-444a-90e1-b776931bd422 · inbound

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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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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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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Observation ba75fa5c-c85a-4113-b380-2b7236876141 · inbound

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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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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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

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:56:44.470334Z

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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

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:56:56.958722Z

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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

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:37:36.866097Z

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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

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:47:59.659895Z

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