Paper Citation Record · LEDGER

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion

As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.06815.

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

pith.paper-citation-record.v1
2506.06815 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:58:16.185561Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy13
unresolved17
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External citation measurements

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Outbound references

Observation a618ab1d-74b6-4f6a-aa46-1b8a9492abd3 · outbound

This paper cites Compound classification using the scikit-learn library.Tutorials in Chemoinformatics, pages 223–239, 2017.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Compound classification using the scikit-learn library.Tutorials in Chemoinformatics, pages 223–239, 2017

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.731512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e4126470-b195-4e9e-a7d7-0c7c2018662a · outbound

This paper cites A consensus-based global optimization method for high dimensional machine learning problems.ESAIM: Control, Optimisation and Calculus of Variations, 27:S5, 2021.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion A consensus-based global optimization method for high dimensional machine learning problems.ESAIM: Control, Optimisation and Calculus of Variations, 27:S5, 2021

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.716671Z

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Observation 749f7995-b674-4411-8b2c-ef67e4a55150 · outbound

This paper cites On Empirical Comparisons of Optimizers for Deep Learning.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion On Empirical Comparisons of Optimizers for Deep Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.049205Z

Source-reported events for the cited work

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Observation 058fbd0c-0cc2-4de5-895d-0a7d88e0471b · outbound

This paper cites Global Optimization via Schr{\"o}dinger-F{\"o}llmer Diffusion.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Global Optimization via Schr{\"o}dinger-F{\"o}llmer Diffusion

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:58:16.420040Z

Source-reported events for the cited work

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Observation f3990ab4-01a3-49e5-aaaa-caf350b92913 · outbound

This paper cites Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.058878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c173e111-0d29-4fc9-beed-6bb978e29e69 · outbound

This paper cites UCI machine learning repository, 2017.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion UCI machine learning repository, 2017

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.701498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e5dbbbb-e828-49c8-a1bd-6a13f0e3652b · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.Journal of machine learning research, 12(7), 2011.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Adaptive subgradient methods for online learning and stochastic optimization.Journal of machine learning research, 12(7), 2011

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.069051Z

Source-reported events for the cited work

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Observation 23380587-d6ac-4373-bc13-41f85cdd9c76 · outbound

This paper cites An entropy approach to the time reversal of diffusion processes.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion An entropy approach to the time reversal of diffusion processes

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.670758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b13557fc-4684-4fba-9d74-a97fa9b19af0 · outbound

This paper cites Schr{\"o}dinger-F{\"o}llmer Sampler: Sampling without Ergodicity.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Schr{\"o}dinger-F{\"o}llmer Sampler: Sampling without Ergodicity

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.078461Z

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Observation a93e3d08-13c8-4146-b0f1-4991d84ef036 · outbound

This paper cites Improving Generalization Performance by Switching from Adam to SGD.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Improving Generalization Performance by Switching from Adam to SGD

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.083181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7601cd32-2502-4cf8-b79f-a7a5c6006401 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Adam: A Method for Stochastic Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.087664Z

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Observation 82fdb5ed-f5a4-4ed6-bb9e-1761c0e87777 · outbound

This paper cites Kirkpatrick, C.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Kirkpatrick, C

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.094260Z

Source-reported events for the cited work

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Observation d70d8832-2728-4fed-abd6-9bba53a78151 · outbound

This paper cites The mnist database of handwritten digits.http://yann.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion The mnist database of handwritten digits.http://yann

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.100379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0951e77-a15c-4bc7-8644-c78f86645bb2 · outbound

This paper cites On the convergence of stochastic gradient descent with adaptive stepsizes.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion On the convergence of stochastic gradient descent with adaptive stepsizes

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.645051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3bbe9e90-d849-4644-996c-1506db90e7be · outbound

This paper cites Decoupled weight decay regularization.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Decoupled weight decay regularization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.111517Z

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Observation b03f8dc1-01f2-4372-b724-064cc0db3672 · outbound

This paper cites Adding Gradient Noise Improves Learning for Very Deep Networks.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Adding Gradient Noise Improves Learning for Very Deep Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.116602Z

Source-reported events for the cited work

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Observation d447581b-dc7f-4ff2-953e-3881ad3736e2 · outbound

This paper cites A method of solving a convex programming problem with con- vergence rate o\bigl(kˆ2\bigr).

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion A method of solving a convex programming problem with con- vergence rate o\bigl(kˆ2\bigr)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.121743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10396d51-d429-435b-833b-301a13b988cd · outbound

This paper cites On the expressive power of deep neural networks.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion On the expressive power of deep neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.126905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86d19016-c968-403e-a6c7-260e89011e89 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.131172Z

Source-reported events for the cited work

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Observation 8f93e91e-24d6-46d0-ae69-37bf85764364 · outbound

This paper cites Rapin and O.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Rapin and O

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.596113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6bc0304f-9fd3-4c39-9113-1bacb2ec4c2d · outbound

This paper cites To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.573312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8b542cb1-ea05-4678-a423-52e148ad34e6 · outbound

This paper cites A stochastic approximation method.The annals of math- ematical statistics, pages 400–407, 1951.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion A stochastic approximation method.The annals of math- ematical statistics, pages 400–407, 1951

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.555049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48705083-0c92-46fc-b12b-4116a7578265 · outbound

This paper cites Rubinstein.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Rubinstein

Reference 23

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Source-reported events for the cited work

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Observation 28bc1a7d-a4eb-4e83-ba50-84a4c4306a03 · outbound

This paper cites A generalized path integral control approach to reinforcement learning.J.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion A generalized path integral control approach to reinforcement learning.J

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.536929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd0d1cf9-d6b5-4cb4-8015-cd079ac1946b · outbound

This paper cites An incremental gradient (-projection) method with momentum term and adaptive stepsize rule.SIAM Journal on Optimization, 8(2):506–531, 1998.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion An incremental gradient (-projection) method with momentum term and adaptive stepsize rule.SIAM Journal on Optimization, 8(2):506–531, 1998

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.516790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cda913e3-6f20-4125-86af-b327f6cab971 · outbound

This paper cites Theoretical guarantees for sampling and inference in generative models with latent diffusions.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Theoretical guarantees for sampling and inference in generative models with latent diffusions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.496861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d689ce5-5c43-4a33-ab78-edf3872d92f4 · outbound

This paper cites Bayesian Learning via Neural Schr\"odinger-F\"ollmer Flows.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Bayesian Learning via Neural Schr\"odinger-F\"ollmer Flows

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.165486Z

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Observation 7deb5e6f-c3e6-4862-aa62-b2b58dd276e7 · outbound

This paper cites Denoising Diffusion Samplers.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Denoising Diffusion Samplers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.170288Z

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Observation 29871469-cd3f-4650-b5c2-3c94f726d87f · outbound

This paper cites Bayesian learning via stochastic gradient langevin dy- namics.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Bayesian learning via stochastic gradient langevin dy- namics

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.476355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dc2446fb-caaa-495c-ab9c-1db78d88089f · outbound

This paper cites Path Integral Sampler: a stochastic control approach for sampling.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion Path Integral Sampler: a stochastic control approach for sampling

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:16.180145Z

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Unavailable: canonical work link unavailable.

Observation 7afd49c4-525f-4f6c-b739-2837a233e0c2 · outbound

This paper cites 10 PATHINTEGRALOPTIMISER A.3.

Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion 10 PATHINTEGRALOPTIMISER A.3

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:16.458186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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