Paper Citation Record · LEDGER

Efficient Training of Neural SDEs Using Stochastic Optimal Control

As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2505.17150.

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

pith.paper-citation-record.v1
2505.17150 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:04:37.897242Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy8
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

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

This paper cites Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit.

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

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:37.835697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc113a1f-53c0-4f72-9540-2f5baa59b684 · outbound

This paper cites Scalable gradients and variational inference for stochastic differential equations.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Scalable gradients and variational inference for stochastic differential equations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:37.843067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6de4d4bc-04f4-41da-84b2-627b639d22f8 · outbound

This paper cites Variational inference for SDEs driven by fractional noise.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Variational inference for SDEs driven by fractional noise

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:38.128233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d313434-7e67-4c76-9a8e-adafee8378cd · outbound

This paper cites Neural markov controlled sde: Stochastic optimization for continuous-time data.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Neural markov controlled sde: Stochastic optimization for continuous-time data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:38.099821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation daea5b7c-3dbf-4f41-aabe-bbb389718781 · outbound

This paper cites Efficient and accurate gradients for neural sdes.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Efficient and accurate gradients for neural sdes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:38.080351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e4d2f29-a067-4879-979f-4a237ae0dcaf · outbound

This paper cites Amortized reparametrization: efficient and scalable variational inference for latent sdes.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Amortized reparametrization: efficient and scalable variational inference for latent sdes

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:38.061135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44edfefc-93ff-40a7-98b0-1a169e3725ea · outbound

This paper cites Variational inference for stochastic differential equations.Annalen der Physik, 531(3):1800233, 2019.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Variational inference for stochastic differential equations.Annalen der Physik, 531(3):1800233, 2019

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:37.870326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7181fe42-223c-4d04-b924-56ce2bc0e64d · outbound

This paper cites Linear theory for control of nonlinear stochastic systems.Physical review letters, 95(20):200201, 2005.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Linear theory for control of nonlinear stochastic systems.Physical review letters, 95(20):200201, 2005

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:38.025669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2443b6ef-0179-4087-a157-1e87f5484820 · outbound

This paper cites Approximate inference for continuous-time markov processes.Bayesian time series models, pages 125–140, 2011.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Approximate inference for continuous-time markov processes.Bayesian time series models, pages 125–140, 2011

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:38.003627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c5c4329-f518-4c9e-9e23-11e03753a313 · outbound

This paper cites Deterministic particle flows for constraining stochastic nonlinear systems.Physical Review Research, 4(4):043035, 2022.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Deterministic particle flows for constraining stochastic nonlinear systems.Physical Review Research, 4(4):043035, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:37.986516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b3c20484-7465-4921-8dec-1e631b9ecd52 · outbound

This paper cites Cam- bridge University Press, 2019.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Cam- bridge University Press, 2019

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:37.968020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b379698-1bfd-4963-9af3-b1a6a7632b19 · outbound

This paper cites Statistical Inference for Stochastic Differential Equations with Memory.

Efficient Training of Neural SDEs Using Stochastic Optimal Control Statistical Inference for Stochastic Differential Equations with Memory

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:37.897242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.