Paper Citation Record · LEDGER

Exact ensemble controllability for neural differential equations via neural interpolation

As of 5 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.21112.

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pith.paper-citation-record.v1
2607.21112 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:36:20.101690Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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Outbound references

Observation b985464a-ae23-424c-9365-4b1a7cb3b52f · outbound

This paper cites Ensemble controllability by lie algebraic methods.ESAIM: Control, Optimisation and Calculus of Variations, 22(4):921–938, 2016.

Exact ensemble controllability for neural differential equations via neural interpolation Ensemble controllability by lie algebraic methods.ESAIM: Control, Optimisation and Calculus of Variations, 22(4):921–938, 2016

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.333162Z

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Observation b5c3d43d-4838-49ca-a91b-395f72299e67 · outbound

This paper cites In- terplay between depth and width for interpolation in neural odes.Neural Networks, 180:106640, 2024.

Exact ensemble controllability for neural differential equations via neural interpolation In- terplay between depth and width for interpolation in neural odes.Neural Networks, 180:106640, 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.381794Z

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Observation fae8b3e4-4782-4b2e-aa52-b0c8ecbc5513 · outbound

This paper cites Multivariate hyperbolic tangent neural network approximation.Computers & Mathematics with Applications, 61(4):809– 821, 2011.

Exact ensemble controllability for neural differential equations via neural interpolation Multivariate hyperbolic tangent neural network approximation.Computers & Mathematics with Applications, 61(4):809– 821, 2011

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.513411Z

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Observation d3c5c2e1-e62c-4a0d-b382-22031cf07efd · outbound

This paper cites Neural ordinary differential equations.Advances in neural informa- tion processing systems, 31, 2018.

Exact ensemble controllability for neural differential equations via neural interpolation Neural ordinary differential equations.Advances in neural informa- tion processing systems, 31, 2018

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.632934Z

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Observation 6fab666a-63b2-4a59-91fa-a402dadb70ff · outbound

This paper cites Interpolation, approximation, and controllability of deep neural networks.SIAM Journal on Control and Optimization, 63:625–649, 2025.

Exact ensemble controllability for neural differential equations via neural interpolation Interpolation, approximation, and controllability of deep neural networks.SIAM Journal on Control and Optimization, 63:625–649, 2025

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.755494Z

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Observation 2354f0c6-18e7-4b8c-9209-41984147d95f · outbound

This paper cites Stabilizing time-varying feedback.IF AC Proceedings Volumes, 28(14):159–166, 1995.

Exact ensemble controllability for neural differential equations via neural interpolation Stabilizing time-varying feedback.IF AC Proceedings Volumes, 28(14):159–166, 1995

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.856349Z

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Observation 64fe02ca-0688-4355-a9b5-ca831319eb68 · outbound

This paper cites Approximation results for neural net- work operators activated by sigmoidal functions.Neural Networks, 44:101– 106, 2013.

Exact ensemble controllability for neural differential equations via neural interpolation Approximation results for neural net- work operators activated by sigmoidal functions.Neural Networks, 44:101– 106, 2013

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:17.983334Z

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Observation cd5c3fe0-1c9c-4d8d-a733-6359e249f1e8 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

Exact ensemble controllability for neural differential equations via neural interpolation Approximation by superpositions of a sigmoidal function

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.151544Z

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Observation 7ab75f39-1c76-401c-abe5-d2662bc1f7e7 · outbound

This paper cites Ensemble controllability of parabolic type equations.Systems & Control Letters, 183:105683, 2024.

Exact ensemble controllability for neural differential equations via neural interpolation Ensemble controllability of parabolic type equations.Systems & Control Letters, 183:105683, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.323431Z

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Observation 6525af5c-67a2-4be8-b2e7-91de883ee998 · outbound

This paper cites Conditions for uni- form ensemble output controllability, and obstruction to uniform ensemble controllability.Mathematical Control and Related Fields, 14(3):1128–1175, 2024.

Exact ensemble controllability for neural differential equations via neural interpolation Conditions for uni- form ensemble output controllability, and obstruction to uniform ensemble controllability.Mathematical Control and Related Fields, 14(3):1128–1175, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.451289Z

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Observation 372c3f92-6e31-4eae-8b7a-39ef57e792d7 · outbound

This paper cites On the turnpike to design of deep neural networks: Explicit depth bounds.IF AC journal of systems and control, 30:100290, 2024.

Exact ensemble controllability for neural differential equations via neural interpolation On the turnpike to design of deep neural networks: Explicit depth bounds.IF AC journal of systems and control, 30:100290, 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.607187Z

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Observation f5057eb9-9ed7-4295-8155-5867077c6264 · outbound

This paper cites Turnpike in optimal control of pdes, resnets, and beyond.Acta Numerica, 31:135–263, 2022.

Exact ensemble controllability for neural differential equations via neural interpolation Turnpike in optimal control of pdes, resnets, and beyond.Acta Numerica, 31:135–263, 2022

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.741187Z

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Observation 895ef75d-b99e-41d9-91f1-1d53389a987c · outbound

This paper cites The finite-time turnpike property in machine learning.Ma- chines, 12(10):705, 2024.

Exact ensemble controllability for neural differential equations via neural interpolation The finite-time turnpike property in machine learning.Ma- chines, 12(10):705, 2024

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.841517Z

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Observation 2de53bad-a241-4345-8232-6a170515ea63 · outbound

This paper cites Optimal control of neural differential equations: The turn- pike property.Available at SSRN 5503851, 2025.

Exact ensemble controllability for neural differential equations via neural interpolation Optimal control of neural differential equations: The turn- pike property.Available at SSRN 5503851, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:18.925395Z

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Observation f18aff74-3935-41f2-9241-d3a9edb8f180 · outbound

This paper cites The turnpike prop- erty for mean-field optimal control problems.European Journal of Applied Mathematics, 35(6):733–747, 2024.

Exact ensemble controllability for neural differential equations via neural interpolation The turnpike prop- erty for mean-field optimal control problems.European Journal of Applied Mathematics, 35(6):733–747, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.024329Z

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Observation 4ef6646c-b288-41e6-b0d7-b2f041f81f43 · outbound

This paper cites Universal approximation of dynamical systems by semiautonomous neural odes and applications.SIAM Journal on Numerical Analysis, 64(1):193–223, 2026.

Exact ensemble controllability for neural differential equations via neural interpolation Universal approximation of dynamical systems by semiautonomous neural odes and applications.SIAM Journal on Numerical Analysis, 64(1):193–223, 2026

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.139511Z

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Observation 29cbb17a-faf9-490c-8b1c-81eb548fe1cb · outbound

This paper cites Unifying machine learning and interpolation theory via interpolating neural networks.Nature Communications, 16(1):8753, 2025.

Exact ensemble controllability for neural differential equations via neural interpolation Unifying machine learning and interpolation theory via interpolating neural networks.Nature Communications, 16(1):8753, 2025

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.251649Z

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Observation 55b42a6d-fe01-49fe-b130-acd556abadb7 · outbound

This paper cites Towards an Optimal Control Perspective of ResNet Training.

Exact ensemble controllability for neural differential equations via neural interpolation Towards an Optimal Control Perspective of ResNet Training

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.409783Z

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Observation f64f1fd9-7ec6-4b97-9223-d627d025477b · outbound

This paper cites Gener- ative models of cell dynamics: from neural odes to flow matching.Com- munications Biology, 2026.

Exact ensemble controllability for neural differential equations via neural interpolation Gener- ative models of cell dynamics: from neural odes to flow matching.Com- munications Biology, 2026

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.615292Z

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Observation ecfe822d-c00f-4d4e-b3e2-53ece30f0cff · outbound

This paper cites Universal approximation power of deep residual neural networks through the lens of control.IEEE Trans- actions on Automatic Control, 68(5):2715–2728, 2023.

Exact ensemble controllability for neural differential equations via neural interpolation Universal approximation power of deep residual neural networks through the lens of control.IEEE Trans- actions on Automatic Control, 68(5):2715–2728, 2023

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.763053Z

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Observation ae10cf47-895e-40bb-8795-49b353130962 · outbound

This paper cites Springer Science & Business Media, 2011.

Exact ensemble controllability for neural differential equations via neural interpolation Springer Science & Business Media, 2011

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:19.940724Z

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Observation 1c3a1d67-2ba3-48d0-b601-e95c1078d41b · outbound

This paper cites Machine learning and control: Foundations, advances, and perspectives.arXiv preprint arXiv:2510.03303, 2025.

Exact ensemble controllability for neural differential equations via neural interpolation Machine learning and control: Foundations, advances, and perspectives.arXiv preprint arXiv:2510.03303, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:20.101690Z

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