Paper Citation Record · LEDGER

Distribution learning via neural differential equations: minimal energy regularization and approximation theory

As of 23 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2502.03795.

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

pith.paper-citation-record.v1
2502.03795 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:56:24.248123Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:59:32.095664Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T23:50:47.739242Z

Reference resolution

34 of 34 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0852fb2b-d02b-40b3-b62d-f7d320b26646 · outbound

This paper cites Albergo and Eric V anden-Eijnden.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Albergo and Eric V anden-Eijnden

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:56:25.151345Z

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Observation 1abfdee5-3956-46d4-b4ac-5b5e529d0f6d · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 3

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Observation 9ae7c729-25ac-4a05-a167-2f550be2e7cf · outbound

This paper cites doi: 10.1007/s10208-023-09630-x.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory doi: 10.1007/s10208-023-09630-x

Reference 4

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Observation 417b81eb-62bc-4ec8-b9d6-14b9e69982ce · outbound

This paper cites doi: 10.1007/978-3-662-00547-7.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory doi: 10.1007/978-3-662-00547-7

Reference 10

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Observation 0fb56a2c-638b-47c3-b5b2-809b98d1244b · outbound

This paper cites Convergence of Continuous Normalizing Flows for Learning Probability Distributions.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 13

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Observation 1812e40c-3114-458b-b376-fad2d91c2392 · outbound

This paper cites Courville.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Courville

Reference 15

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Observation 2b054356-7796-41dc-98d0-3241b86143fa · outbound

This paper cites Convergence Analysis of Probability Flow ODE for Score-based Generative Models.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Convergence Analysis of Probability Flow ODE for Score-based Generative Models

Reference 17

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Observation 94607493-f212-458f-a5c1-d1beb0739447 · outbound

This paper cites Normalizi ng ﬂows: An introduction and review of current methods.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Normalizi ng ﬂows: An introduction and review of current methods

Reference 19

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Observation 132417b7-2224-4a3e-9179-56391e2b5c8d · outbound

This paper cites doi: 10.1109/tpami.2020.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory doi: 10.1109/tpami.2020

Reference 20

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Observation f363e014-9d0f-4fb2-9dfb-deb7bb386228 · outbound

This paper cites A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 21

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Observation 4ebf878f-3ca8-4e6d-b545-97e44a916ccb · outbound

This paper cites A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 22

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Observation cbf33116-c7c8-4cdb-8f93-a0457d3538c5 · outbound

This paper cites Deep Learning via Dynamical Systems: An Approximation Perspective.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Deep Learning via Dynamical Systems: An Approximation Perspective

Reference 23

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This paper cites 42 Xingchao Liu, Chengyue Gong, and Qiang Liu.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory 42 Xingchao Liu, Chengyue Gong, and Qiang Liu

Reference 24

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Observation 2e882501-5d5a-43c8-a8f7-2fa89f79fb19 · outbound

This paper cites Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:56:25.088356Z

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Observation 5f92a6e6-7911-4225-9bdd-b21760aed190 · outbound

This paper cites U RL http://dx.doi.org/10.1007/978-3-319-11259-6_23-1.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory U RL http://dx.doi.org/10.1007/978-3-319-11259-6_23-1

Reference 26

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Observation baaf128a-27e6-4109-b2a2-ec0dcb82edd8 · outbound

This paper cites Distribution learning via neural differential equations: a nonparametric statistical perspective.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Distribution learning via neural differential equations: a nonparametric statistical perspective

Reference 27

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Observation 9b4edbcd-c32a-481a-95b6-7780586b83f4 · outbound

This paper cites URL https://link.springer.com/content/pdf/10.1007%2F978-3-030-38438-8.pdf.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory URL https://link.springer.com/content/pdf/10.1007%2F978-3-030-38438-8.pdf

Reference 29

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Observation aa1e9a0b-95a7-4241-a4fe-58641792ec5b · outbound

This paper cites URL https://doi.org/10.1137/21m1411433.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory URL https://doi.org/10.1137/21m1411433

Reference 30

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This paper cites URL https://doi.org/10.1007/s10851-019-00903-1.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory URL https://doi.org/10.1007/s10851-019-00903-1

Reference 31

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Observation 033aaaf8-9a8b-4674-9d59-02b44e25cfc9 · outbound

This paper cites Y ang Song, Jascha Sohl-Dickstein, Diederik P.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Y ang Song, Jascha Sohl-Dickstein, Diederik P

Reference 32

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Observation 7714474f-e85f-4952-9d93-eb4dd743d70a · outbound

This paper cites Sparse approximation of tri angular transports, part i: The ﬁnite-dimensional case.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Sparse approximation of tri angular transports, part i: The ﬁnite-dimensional case

Reference 35

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Observation 88805e51-5870-4821-9524-42ec63b53e05 · outbound

This paper cites URL https://doi.org/10.1007/s002110050002.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory URL https://doi.org/10.1007/s002110050002

Reference 2000

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Observation 1c6e691b-aacb-44ac-88a7-cf586f708d15 · outbound

This paper cites 40 Y ann Brenier.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory 40 Y ann Brenier

Reference 2007

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Observation 953a1ff6-a5ae-445d-93dc-4626c3032662 · outbound

This paper cites Universal Approximation Property of Neural Ordinary Differential Equations.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Universal Approximation Property of Neural Ordinary Differential Equations

Reference 2010

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This paper cites doi: https://doi.org/10.1016/j.jcp.201 2.07.022.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory doi: https://doi.org/10.1016/j.jcp.201 2.07.022

Reference 2012

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Observation 22069775-a91b-4346-ad6c-a9908f67b724 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory NICE: Non-linear Independent Components Estimation

Reference 2015

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Distribution learning via neural differential equations: minimal energy regularization and approximation theory Glow: Generative ﬂow wi th invertible 1x1 convolutions

Reference 2016

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Observation 224a4069-906e-4f37-ac68-0efaa94bef21 · outbound

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Distribution learning via neural differential equations: minimal energy regularization and approximation theory U RL https://doi.org/10.1016/j.neunet.2017.07.002

Reference 2017

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Distribution learning via neural differential equations: minimal energy regularization and approximation theory Neural Ordinary Differential Equations

Reference 2018

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Distribution learning via neural differential equations: minimal energy regularization and approximation theory URL https://doi.org/10.24963/ijcai.2019/103

Reference 2019

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Observation 3dae8e99-7908-4d8b-a877-4181574450d0 · outbound

This paper cites Convergence of Continuous Normalizing Flows for Learning Probability Distributions.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 2020

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Distribution learning via neural differential equations: minimal energy regularization and approximation theory Convergence Analysis of Probability Flow ODE for Score-based Generative Models

Reference 2021

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This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2023

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Distribution learning via neural differential equations: minimal energy regularization and approximation theory Unresolved cited work

Reference 2024

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Pith citing papers

Observation a1115eb3-5b2e-420a-bc7e-8ac6f6f8fb43 · inbound

Optimal Scheduling of Dynamic Transport cites this paper.

Optimal Scheduling of Dynamic Transport Distribution learning via neural differential equations: minimal energy regularization and approximation theory

Reference 31

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Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs cites this paper.

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs Distribution learning via neural differential equations: minimal energy regularization and approximation theory

Reference 23

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