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Paper Citation Record · LEDGER

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces

As of 12 August 2026, this Paper Citation Record lists 100 of 129 outbound references and 0 inbound Pith citation observations for arXiv:2607.23110.

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

pith.paper-citation-record.v1
2607.23110 v1

Coverage vector

measured 100 of 129 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T03:42:48.069088Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

100 of 129 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved98
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 26137a15-b37b-4b6d-a873-785112d9ba18 · outbound

This paper cites arXiv preprint arXiv:2510.02757 , year=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces arXiv preprint arXiv:2510.02757 , year=

Reference 1

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Observation b33620d0-525a-426f-8752-ee7e2f80fd02 · outbound

This paper cites Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages =

Reference 2

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Observation 980e340e-2c42-4988-bbfc-567024e6ccc8 · outbound

This paper cites Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics , pages =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics , pages =

Reference 3

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Observation 2ead0ada-7525-4740-b902-6427b53145a8 · outbound

This paper cites Gaussian Processes in Machine Learning.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Gaussian Processes in Machine Learning

Reference 4

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Observation 8de36030-0ecc-4486-8f9c-87b192d43d28 · outbound

This paper cites IEEE signal processing magazine , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces IEEE signal processing magazine , volume=

Reference 5

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Observation 86fb3dd6-1a38-4610-aced-ff32afeb9240 · outbound

This paper cites Journal of basic Engineering , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Journal of basic Engineering , volume=

Reference 6

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Observation 3f3aae71-8432-4f5d-a78b-f0264f9f8afb · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 7

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Observation ff978c4d-fa08-4834-a05c-6ee709fabe24 · outbound

This paper cites Black box variational inference for state space models.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Black box variational inference for state space models

Reference 8

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source=arxiv_source observed=2026-08-01T03:42:47.475299Z digest=sha256:ddd14ddb9a7d69d4055790fe8130432d7dd1e4dcbeea540c1310880b6f7fd787

Observation 11f5ffd9-cdb2-4eb7-9627-d55e0fc5a237 · outbound

This paper cites International conference on artificial intelligence and statistics , pages=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces International conference on artificial intelligence and statistics , pages=

Reference 9

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Observation 4e77a049-762a-4f97-88ad-0769855e2ba3 · outbound

This paper cites Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

Reference 10

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Observation 180390dc-c23a-4510-8bf5-3d9d3338eb30 · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 11

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Observation d730964d-f9ee-4328-b1d9-3f4351160e41 · outbound

This paper cites IEEE Transactions on Signal Processing , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces IEEE Transactions on Signal Processing , volume=

Reference 12

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Observation 262cc9ca-4a62-4ea7-8c38-cfb2eef579c7 · outbound

This paper cites Tambe-Ndonfack , journal =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Tambe-Ndonfack , journal =

Reference 13

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Observation c48d1906-1e44-4b8a-a0b4-eb7823b04ae1 · outbound

This paper cites Layer Normalization.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Layer Normalization

Reference 14

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 15

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Observation fba3cfd7-8f78-41da-a49b-dcec4e1028b3 · outbound

This paper cites Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

Reference 16

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Observation fc7881e9-2049-45ab-a22a-d17af7b8515d · outbound

This paper cites Journal of Machine Learning Research , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Journal of Machine Learning Research , volume=

Reference 17

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Observation b2d6d9d7-568c-4791-a75d-bdc9b40c01e4 · outbound

This paper cites 2021 , publisher=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2021 , publisher=

Reference 18

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Observation 9cdeadaa-35dd-48d0-b9e5-bd7f8941ae3f · outbound

This paper cites and Rao, B.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces and Rao, B

Reference 19

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Observation 34c31b23-430a-4466-bac8-590e42833513 · outbound

This paper cites Math24 , year=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Math24 , year=

Reference 20

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Observation 7cc11006-7d06-4270-95fe-a642e98e9df2 · outbound

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2021 , publisher=

Reference 21

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2021 , organization=

Reference 22

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Observation 3652567b-0118-4cb9-b847-0635192ad44b · outbound

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2021 , organization=

Reference 23

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 24

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Observation 31a7e23b-b2c3-42af-ae56-aef3db6b9b9a · outbound

This paper cites Extending Path-Dependent.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Extending Path-Dependent

Reference 25

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Observation 51b2f42c-a0ab-41a0-99a2-5df7a664d630 · outbound

This paper cites Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

Reference 26

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Observation dcd8edf9-d55d-4bb8-a0e7-557513ed1d54 · outbound

This paper cites A first course in dynamics: With a panorama of recent developments , DOI=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces A first course in dynamics: With a panorama of recent developments , DOI=

Reference 27

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Observation dd5e5bcd-f916-44c5-aa94-e6cfcc4ac9df · outbound

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2009 , publisher=

Reference 28

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Observation 8aaee213-6d04-4169-b4e2-f3d67ecf8ff4 · outbound

This paper cites Algorithmic Finance , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Algorithmic Finance , volume=

Reference 29

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Observation b1856237-3799-49ac-82c3-4e2112a0b212 · outbound

This paper cites Applied Soft Computing , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Applied Soft Computing , volume=

Reference 30

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Observation 2e881c53-84c1-4488-aed2-0e8b2d48e386 · outbound

This paper cites 2017 IEEE 19th conference on business informatics (CBI) , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2017 IEEE 19th conference on business informatics (CBI) , volume=

Reference 31

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Observation 9b230461-3bf6-446d-a848-66fbec254143 · outbound

This paper cites IEEE Transactions on Emerging Topics in Computational Intelligence , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces IEEE Transactions on Emerging Topics in Computational Intelligence , volume=

Reference 32

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This paper cites 2017 IEEE Symposium Series on Computational Intelligence (SSCI) , pages=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2017 IEEE Symposium Series on Computational Intelligence (SSCI) , pages=

Reference 33

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Journal of Forecasting , volume=

Reference 34

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Observation d0141839-3b27-415c-88ee-b7a65694469d · outbound

This paper cites IEEE Transactions on Signal Processing , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces IEEE Transactions on Signal Processing , volume=

Reference 35

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Observation fc36ae43-9292-4f02-83bb-b00979804f43 · outbound

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2021 , url=

Reference 36

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Bounded Variation and Around , year=

Reference 37

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This paper cites Advances in Psychology , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Advances in Psychology , volume=

Reference 38

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Observation 014d50ec-2b77-4f75-b99a-0d57b8a93e51 · outbound

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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 1985 , institution=

Reference 39

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source=arxiv_source observed=2026-08-01T03:42:47.557969Z digest=sha256:57eea4e4e78ff219d0b8705d9aa8c061a17b5f1496e42060b12beff6ae52f8a3

Observation 2ba6226c-f7d4-487f-9a94-4d8f65fc4e9f · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Advances in Neural Information Processing Systems , volume=

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source=arxiv_source observed=2026-08-01T03:42:47.560409Z digest=sha256:0987686d669a355f2d79cab3d1f1f9321584b807b835de7804f699176cab6a56

Observation 94571dec-e12c-4b57-9988-4a427bb3f9bd · outbound

This paper cites Scientific Reports , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Scientific Reports , volume=

Reference 41

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source=arxiv_source observed=2026-08-01T03:42:47.563282Z digest=sha256:57506a1fe03a0098812ec26410c3518921edf90e6ca40fe74097b4ca54ec9269

Observation 57778c54-d91f-4ab5-bff3-59fad8502d9f · outbound

This paper cites arXiv , year=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces arXiv , year=

Reference 42

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source=arxiv_source observed=2026-08-01T03:42:47.566040Z digest=sha256:b7db522901a4d863b404786b02a813861fb65214ff59d1d02b9743cec94be6b5

Observation 887398cf-70fb-4659-aaff-2338a4f924e6 · outbound

This paper cites Probability and Measure , year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Probability and Measure , year =

Reference 43

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source=arxiv_source observed=2026-08-01T03:42:47.568697Z digest=sha256:060bb4cc2afdc5c6d692ac14db74bfcbfea16a3a424fed4522d767349c12c140

Observation c8a6c804-3f86-458b-ad81-b83a0325e3d1 · outbound

This paper cites Multivariate statistics: A vector space approach , year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Multivariate statistics: A vector space approach , year =

Reference 44

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source=arxiv_source observed=2026-08-01T03:42:47.571290Z digest=sha256:df1ca3eef119da0e7486ac6d4ffe5422228061f34b8cd87c0ca2cfdc051ab70f

Observation b1b8a568-f86f-421d-bcc2-2fc1716ff8de · outbound

This paper cites Journal of Mathematical Analysis and Applications , volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Journal of Mathematical Analysis and Applications , volume =

Reference 45

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source=arxiv_source observed=2026-08-01T03:42:47.573955Z digest=sha256:af195f61a88ada802779d44ac5423435a810676ff8e010e7c78dd94c88cd93f2

Observation 7080dd68-34fb-48b2-a0f1-c99f0ad851de · outbound

This paper cites 2019 , journal=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2019 , journal=

Reference 46

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source=arxiv_source observed=2026-08-01T03:42:47.576613Z digest=sha256:54a5e68b0ed50b42b551f8b4d98d840db2e03500925dd00e78df139f6557e15d

Observation 93acefae-3620-4a1f-81f4-392e382caf56 · outbound

This paper cites 2020 , journal=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2020 , journal=

Reference 47

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source=arxiv_source observed=2026-08-01T03:42:47.579170Z digest=sha256:5e7c9ed199f09fb84004f2ce2c93d6de4d9f81baebb4507b69a67626d495f168

Observation 664fa1f9-4c89-41f1-8152-abee2dec4206 · outbound

This paper cites Kiraly and Harald Oberhauser , title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Kiraly and Harald Oberhauser , title =

Reference 48

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source=arxiv_source observed=2026-08-01T03:42:47.581734Z digest=sha256:47ccfc27ca53fc61def700fe548852719c07dad05522e18d96d8656ad63761d9

Observation 8f4f8e8e-b27f-4271-8adb-57d12fcc53bf · outbound

This paper cites and Caruana, Michael J.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces and Caruana, Michael J

Reference 49

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source=arxiv_source observed=2026-08-01T03:42:47.584553Z digest=sha256:93a1da65ed03cf330fdcfe031447037e9b7deca81695f01d912c1ee9a89599e6

Observation 3c60e22a-e6e4-4353-9833-121bb49add3b · outbound

This paper cites Uniqueness for the signature of a path of bounded variation and the reduced path group , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Uniqueness for the signature of a path of bounded variation and the reduced path group , volume=

Reference 50

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source=arxiv_source observed=2026-08-01T03:42:47.587487Z digest=sha256:a5387f5e64eeb8c7729788760efb3c6fb20c99d47dd4a6b62ae7ae58f03a579b

Observation 1e6fb2b6-2b2a-40c8-9472-be8793cc0ec0 · outbound

This paper cites 2016 , journal=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2016 , journal=

Reference 51

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source=arxiv_source observed=2026-08-01T03:42:47.590093Z digest=sha256:df45de3ddfe530e597ce3a26c1dacf29527a79a906601286e4fc1f5ac67233c0

Observation ae2fa6c9-b65a-4dc9-a0c6-af9d6d59c873 · outbound

This paper cites 2016 , journal=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2016 , journal=

Reference 52

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source=arxiv_source observed=2026-08-01T03:42:47.592794Z digest=sha256:dc06d79aa58ae135817ef48ee197ea10edcc1d71a735fc7ddea515b88d3738be

Observation e034e85d-bb53-4d71-afb2-efe4cc4e132f · outbound

This paper cites 2018 , eprint=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2018 , eprint=

Reference 53

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source=arxiv_source observed=2026-08-01T03:42:47.595373Z digest=sha256:516da5d83d9f952ec874f6520e8e1224e2977cb5e87306f21751ed4a465b5508

Observation fcb33cd4-43d3-4372-86ec-eafd122eb334 · outbound

This paper cites 2016 , eprint=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces 2016 , eprint=

Reference 54

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source=arxiv_source observed=2026-08-01T03:42:47.597989Z digest=sha256:baf9b0911b2e04ba236b1ddb7c19f862444ea4fc312ac77cdb76a9523b93c036

Observation 2e8ecf2c-2930-41a0-93ff-462ab2e95c5b · outbound

This paper cites Finance and Stochastics , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Finance and Stochastics , volume=

Reference 55

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source=arxiv_source observed=2026-08-01T03:42:47.600624Z digest=sha256:3b662576e6d445b2a0a7bd782351115607c68a4265e56277580233092e1e2773

Observation 17f16ef1-b990-4def-8244-0d4bb8a161f8 · outbound

This paper cites Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics , volume=

Reference 56

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source=arxiv_source observed=2026-08-01T03:42:47.603264Z digest=sha256:054e4ccdd6e1a4b83eb51cda27a0d0424ad8dfca1c9104e6b5778d14d35d5180

Observation 7a9f5a7a-50f8-44ce-b1d1-0414720ff242 · outbound

This paper cites BOUNDS AND ESTIMATORS OF A BASIC CONSTANT IN EXTREME VALUE THEORY OF.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces BOUNDS AND ESTIMATORS OF A BASIC CONSTANT IN EXTREME VALUE THEORY OF

Reference 57

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source=arxiv_source observed=2026-08-01T03:42:47.605810Z digest=sha256:b8f231383419e717dbabaeea3ac76cb7decbf8de00757de4e73f5fe8895ec26f

Observation 595eb5e0-f04a-4868-bd03-96a92022c0a7 · outbound

This paper cites Statistics & Probability Letters , volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Statistics & Probability Letters , volume =

Reference 58

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source=arxiv_source observed=2026-08-01T03:42:47.608129Z digest=sha256:5860c252d85f0a8164c973ced5de4f7b11dc7969f85e5b250d905b65a59f2c8c

Observation 9344bf51-d993-4f3b-b4ec-aba807f3e52f · outbound

This paper cites Stochastics , volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Stochastics , volume =

Reference 59

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source=arxiv_source observed=2026-08-01T03:42:47.610552Z digest=sha256:e7d7d305766b13924742fdfcf95c046c75cc1624ddd818c341c1c165d9405834

Observation b6fa5b6d-8026-4041-b32b-d189fb26174e · outbound

This paper cites , address =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces , address =

Reference 60

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source=arxiv_source observed=2026-08-01T03:42:47.612968Z digest=sha256:b34dc7fc246caf5fc40b6cb432ee9dbd765fd364853e7b5451546ee8513ee7b6

Observation af3a5f7a-7f3c-40c7-be69-4cf01b774554 · outbound

This paper cites Bernoulli , number =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Bernoulli , number =

Reference 61

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source=arxiv_source observed=2026-08-01T03:42:47.615337Z digest=sha256:b6319abfe70445c32f666b42fb0c2c39019d57bc61948b81ce232fea560d48a0

Observation f5760d2d-d614-448b-871e-646d55368d05 · outbound

This paper cites Prediction Law of fractional.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Prediction Law of fractional

Reference 62

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source=arxiv_source observed=2026-08-01T03:42:47.617618Z digest=sha256:d8776b6c4b4aefd087326f5c797958d91a15075123099eea0da08ea43f371e61

Observation c9c0f125-6bc1-4f89-b5c3-81b892b434c6 · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 63

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source=arxiv_source observed=2026-08-01T03:42:47.620028Z digest=sha256:a5d6ce96d6a284b3b4ea89e6f5f8d6a231e1a057e881af983986c4ee9e91a889

Observation 9c0b9cd9-52bc-4419-931d-26a59a7782e6 · outbound

This paper cites Springer , Title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Springer , Title =

Reference 64

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source=arxiv_source observed=2026-08-01T03:42:47.622284Z digest=sha256:4fe0cc00dccdbc32873726cec453dfaf64b47dd46e12b4c54cf9b69f8dfd35cf

Observation 7a8581af-7d29-4c26-b4c0-0b986f9d9319 · outbound

This paper cites Goldberger and Luis A.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Goldberger and Luis A

Reference 65

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source=arxiv_source observed=2026-08-01T03:42:47.624736Z digest=sha256:f0b5f942468fec4cfab19d60372a19d00a45ed67884f486cc73bcc2cf5b13350

Observation dd018de0-d0cc-4fad-90e3-374fe6a2c873 · outbound

This paper cites Andersen and Tim Bollerslev , Date-Added =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Andersen and Tim Bollerslev , Date-Added =

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source=arxiv_source observed=2026-08-01T03:42:47.627385Z digest=sha256:3c16118f1cb7cbdc8b5f1997922fc9d726b9c77a737d773f8b83b0dc750857a1

Observation 44aad147-9101-40b9-a2c9-4621b4bde5fa · outbound

This paper cites Porcupine Neural Networks: (Almost) All Local Optima are Global.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Porcupine Neural Networks: (Almost) All Local Optima are Global

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source=arxiv_source observed=2026-08-01T03:42:47.635030Z digest=sha256:a64fda5dd21cffb845d24c447aabf66bb9f9b0f7bec0361b4a8e28e141e95fe4

Observation 73145538-093e-4987-a96c-4861f2d1c1e4 · outbound

This paper cites Advances in Neural Information Processing Systems , Title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Advances in Neural Information Processing Systems , Title =

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source=arxiv_source observed=2026-08-01T03:42:47.647089Z digest=sha256:f02118ce876d92456240ce1688faf20c57d5997f87fe1794af67ab52a0ace303

Observation 9c942d2e-73b4-4e95-9795-084ee70a84b5 · outbound

This paper cites Journal of Optimization Theory and applications , Title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Journal of Optimization Theory and applications , Title =

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source=arxiv_source observed=2026-08-01T03:42:47.658458Z digest=sha256:d621c121129b59332171b328b041458c4b82be627cb8281813573513854136de

Observation eb015bad-675d-4a8c-99e0-780b6cbd57f0 · outbound

This paper cites International Conference on Learning Representations , year=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces International Conference on Learning Representations , year=

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source=arxiv_source observed=2026-08-01T03:42:47.669091Z digest=sha256:4779e2ae58cfd5af903a1a67e3f61331eaa520582b372a426529598d8b3a23c0

Observation 869a6fc5-3fac-49c3-9d3c-b9c7f244396c · outbound

This paper cites Monte Carlo Methods and Applications , Title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Monte Carlo Methods and Applications , Title =

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source=arxiv_source observed=2026-08-01T03:42:47.681160Z digest=sha256:f26f88fde20e4ed07708fb1eee71e979fd452caf932b317d752796915db9decf

Observation a96fc6d5-1788-423c-a7bf-97520a3d8475 · outbound

This paper cites Efficient Simulation of the Heston Stochastic Volatility Model , Year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Efficient Simulation of the Heston Stochastic Volatility Model , Year =

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source=arxiv_source observed=2026-08-01T03:42:47.693116Z digest=sha256:c7a5529e06a36442ad8875cc0857fd448aec2fe4104d300c0f7e4c093c7028b7

Observation 45f65534-8890-489e-b9f3-c73132ff62bf · outbound

This paper cites Islam and C.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Islam and C

Reference 73

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source=arxiv_source observed=2026-08-01T03:42:47.706412Z digest=sha256:5ddfeabf57a44a015a914cbc20af96c92407926f171375358774ed4bb06209ca

Observation df058376-878b-47c9-abd0-65418c52d39c · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 74

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source=arxiv_source observed=2026-08-01T03:42:47.717987Z digest=sha256:129b4b7fc0e12b69dfa8afe47152f6503dbd5a35eed17a7d6651b7034eed365f

Observation c238d999-b835-4d40-adc0-dffea633e32d · outbound

This paper cites A simplified analytical approach for pricing discretely-sampled variance swaps with stochastic volatility , Year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces A simplified analytical approach for pricing discretely-sampled variance swaps with stochastic volatility , Year =

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source=arxiv_source observed=2026-08-01T03:42:47.783344Z digest=sha256:529b65ee948759d206b1a11777d3f1615c3d22469e4e7b09a70c0d115aa03c9d

Observation 39dd3132-b9bd-4e7a-8ba2-0431e1b27629 · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 76

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source=arxiv_source observed=2026-08-01T03:42:47.837262Z digest=sha256:60818de35428bd09953f66bf08246da2bd9877d08bb89ee0da9985b77d1fe391

Observation 72b371c5-2e5b-42ab-9972-86b0cbfbfb7f · outbound

This paper cites Monte Carlo Methods and Applications , number =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Monte Carlo Methods and Applications , number =

Reference 77

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source=arxiv_source observed=2026-08-01T03:42:47.903702Z digest=sha256:19acd4daeebcfe3d4e1feda6151b79834cc45fbfcbf3162080c93fc98895fbbf

Observation 49033c73-3f22-4409-9f8e-5c6bdeb57054 · outbound

This paper cites Wiley , Title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Wiley , Title =

Reference 78

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source=arxiv_source observed=2026-08-01T03:42:47.922353Z digest=sha256:b2de4cb7f92ac4050dae4e675620e2d4d5cdb193f3a5bcdb8f70b94d6306c9ed

Observation 14c15fc5-bf44-4bb7-a7d5-f6c96aa7555f · outbound

This paper cites Probability in Banach spaces , Volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Probability in Banach spaces , Volume =

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source=arxiv_source observed=2026-08-01T03:42:47.947256Z digest=sha256:4686c52147acb926a54f76cc670ad5f94535885bad6632da9113095298300c71

Observation 41440454-9243-47ef-903b-f5e0022de17b · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Advances in Neural Information Processing Systems , volume=

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source=arxiv_source observed=2026-08-01T03:42:48.003421Z digest=sha256:be583c63f6c050a2318d7cb7ccffcea487f75d91b25b433214b6a5789c3584d7

Observation 0d801120-71a3-4658-980f-12b9923b987d · outbound

This paper cites Adam: A Method for Stochastic Optimization , Year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Adam: A Method for Stochastic Optimization , Year =

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source=arxiv_source observed=2026-08-01T03:42:48.019780Z digest=sha256:97c0b4d697d8fe12cb9e55e055329da1a839bc1f937ff53b4edbde6861e6efe3

Observation ed30be94-770c-4108-92c3-24767b4c2e4e · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library , Year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces PyTorch: An Imperative Style, High-Performance Deep Learning Library , Year =

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source=arxiv_source observed=2026-08-01T03:42:48.022863Z digest=sha256:1ce351d44a782d34d573bb32f80a02c720e108c16b71c5b69f647939c7ee22d6

Observation 4e513fda-32ae-4320-af70-d6bcca1b91cb · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 83

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source=arxiv_source observed=2026-08-01T03:42:48.025724Z digest=sha256:8aef7a04cde577b319e8f61b5979ca0eab9531cb85d6175d6a47de3fe5fbc470

Observation 041db410-215a-41c9-8799-19d29f8107b1 · outbound

This paper cites Herrera and F.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Herrera and F

Reference 84

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source=arxiv_source observed=2026-08-01T03:42:48.028440Z digest=sha256:c5af8c004fccdbe7d63ee69837cd0c601019eb2ccbd471acb3493089f8ae71ec

Observation 5d4b327f-639b-4fc2-95a3-6e2f96996764 · outbound

This paper cites Neural Stochastic Differential Equations: Deep Latent.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Neural Stochastic Differential Equations: Deep Latent

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Observation feb85be3-7d36-4bf6-995f-15f27d1ba90e · outbound

This paper cites Symposium on Advances in Approximate Bayesian Inference , pages=.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Symposium on Advances in Approximate Bayesian Inference , pages=

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Observation 254d73ba-3ffc-4876-bbfc-ecc3bd42423f · outbound

This paper cites and Pavliotis, G.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces and Pavliotis, G

Reference 87

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source=arxiv_source observed=2026-08-01T03:42:48.036630Z digest=sha256:43b0567412a0823eb0646b11424927b8148431156bfce224367fa55231509044

Observation 1fd61874-a4ab-4241-b3df-11fc06c9f727 · outbound

This paper cites Comparative study of estimation methods for continuous time stochastic processes , Volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Comparative study of estimation methods for continuous time stochastic processes , Volume =

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source=arxiv_source observed=2026-08-01T03:42:48.039336Z digest=sha256:9cb504f0e19fd55ccbc7be4168ed7af8b32ecd837a899912c0a09913c5edbe12

Observation a3159aca-18d4-436f-89ba-d193fa42203f · outbound

This paper cites Stan Hurn and K.A.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Stan Hurn and K.A

Reference 89

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source=arxiv_source observed=2026-08-01T03:42:48.041842Z digest=sha256:73e0e939a817ac2d663f8841f54549115bd16a4c11af986580d78221e67708d6

Observation 7de90844-bf2a-42f3-b326-b0e94a776b7d · outbound

This paper cites Assessing Asset-Liability Risk with Neural Networks , Volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Assessing Asset-Liability Risk with Neural Networks , Volume =

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source=arxiv_source observed=2026-08-01T03:42:48.044243Z digest=sha256:c9e0acc3968206e3123e08cab5b421ff02f3441772f89f89cfa0e871f97ffb62

Observation fe6c29f9-df56-43f1-8d9f-43ba7e26caf5 · outbound

This paper cites Local Lipschitz Bounds of Deep Neural Networks.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Local Lipschitz Bounds of Deep Neural Networks

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source=arxiv_source observed=2026-08-01T03:42:48.046690Z digest=sha256:a85bbb15369dc2c681e7080fac929ae26b844b69fc1fdf145305ea3c36cfbbd4

Observation 11d34b38-f18f-41ff-acc3-53c8462f499a · outbound

This paper cites NeurIPS , Title =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces NeurIPS , Title =

Reference 92

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source=arxiv_source observed=2026-08-01T03:42:48.049374Z digest=sha256:9e72a7f1d8a2fb876b04a3247b9b8e36c051c15bd6be5689c3f0d32f9d0c91a3

Observation 01a10c51-38e7-48ed-b26d-34804b11f744 · outbound

This paper cites Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception , Volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception , Volume =

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source=arxiv_source observed=2026-08-01T03:42:48.051851Z digest=sha256:e6640582e75f1c11e9e93960def4b18ed5b700def3d7b9eb4b5ee24d7f08e334

Observation 36fde2e2-9dc5-412d-ac8d-ea37a4f51775 · outbound

This paper cites Golightly and D.J.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Golightly and D.J

Reference 94

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source=arxiv_source observed=2026-08-01T03:42:48.054168Z digest=sha256:fd10e2da704bd54b8e18f2327516061402cc9031580e4197ce2443bf33a34fc3

Observation 9dcbdd99-b738-4db8-b139-a2bce695c048 · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 95

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source=arxiv_source observed=2026-08-01T03:42:48.056668Z digest=sha256:cd8315460eb4404951ca6bd16bd2b5ddead1ecdf174b11687806ea0fb2aa5209

Observation 36620900-0c73-4f0e-b5a9-03449a45f712 · outbound

This paper cites Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals , Volume =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals , Volume =

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source=arxiv_source observed=2026-08-01T03:42:48.059089Z digest=sha256:9078633121299463a7ef1d4772462501640ed0bfe8d07b7a69e13bb321fb722a

Observation fbd4d0ce-d5a8-4850-9cb4-cbc048d6f2eb · outbound

This paper cites Stephen McGough and Dennis Prangle , Booktitle =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Stephen McGough and Dennis Prangle , Booktitle =

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source=arxiv_source observed=2026-08-01T03:42:48.061824Z digest=sha256:e12299d862cd911e4b5fdf4cb39907e694f1daa0aed1eaac3b70978526070f8d

Observation 960e4a8d-e761-4673-8bac-65ea64fc73d8 · outbound

This paper cites Variational Inference for Diffusion Processes , Year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Variational Inference for Diffusion Processes , Year =

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source=arxiv_source observed=2026-08-01T03:42:48.064268Z digest=sha256:ca8f9568787e7c394ba7f7ac330d5db323c8cfc59b00ddd8aec7a15a80a72278

Observation ee8f6628-029a-4d72-a18c-ff079c37463a · outbound

This paper cites an unresolved cited work.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Unresolved cited work

Reference 99

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source=arxiv_source observed=2026-08-01T03:42:48.066693Z digest=sha256:17826059f7371f2937a4019bb52e08ae763eaeab0f04e98be6eb6136dcc879a4

Observation 2aec6c0e-b610-44ef-b0d3-576e2ca44044 · outbound

This paper cites Stock market prediction with deep learning: A character-based neural language model for event-based trading , Year =.

Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces Stock market prediction with deep learning: A character-based neural language model for event-based trading , Year =

Reference 100

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source=arxiv_source observed=2026-08-01T03:42:48.069088Z digest=sha256:dc34796a0aeefb2f0b2aead9cbef5e95ebd3e5e14e23a2289c50aad6b4fe6b8a

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