Paper Citation Record · LEDGER

Port-Hamiltonian Approach to Neural Network Training

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1909.02702.

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

pith.paper-citation-record.v1
1909.02702 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:48:03.959164Z

measured 33 of 33 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.

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Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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verified fuzzy24
unresolved9
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External citation measurements

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Outbound references

Observation e92c9926-05c2-4edd-885f-9aa2fc565a41 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Port-Hamiltonian Approach to Neural Network Training Multilayer feedforward networks are universal approximators

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00da5a1a-c493-494a-9bf7-b21e62fa5714 · outbound

This paper cites Learning internal representations by error propagation.

Port-Hamiltonian Approach to Neural Network Training Learning internal representations by error propagation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.820017Z

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Observation 323441c4-a283-45a3-89e9-13fa5c0c0c82 · outbound

This paper cites Mask r-cnn.

Port-Hamiltonian Approach to Neural Network Training Mask r-cnn

Reference 3

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Observation 1c0615ef-5c5c-4fad-8506-242d83db7619 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Port-Hamiltonian Approach to Neural Network Training Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.829332Z

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Observation bc47556c-55d5-4d05-9756-6c286692d844 · outbound

This paper cites Deep residual learning for image recognition.

Port-Hamiltonian Approach to Neural Network Training Deep residual learning for image recognition

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.834695Z

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Observation 9c2c31fc-1064-4ce2-87de-a2b5a736da76 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Port-Hamiltonian Approach to Neural Network Training BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.839761Z

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Observation 258976d9-9385-4555-8746-70c189faac06 · outbound

This paper cites Kingma and Jimmy Ba.

Port-Hamiltonian Approach to Neural Network Training Kingma and Jimmy Ba

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.352139Z

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Observation 8c16ac56-f62e-463b-9d6e-7fccf74e2f35 · outbound

This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.

Port-Hamiltonian Approach to Neural Network Training Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.339000Z

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Observation 43ec225f-5dfe-4f7b-9cc2-091a0ccb5c79 · outbound

This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

Port-Hamiltonian Approach to Neural Network Training On the Variance of the Adaptive Learning Rate and Beyond

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.852071Z

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Observation 5fb6579b-646e-4348-a907-eb8caf305569 · outbound

This paper cites Visualizing the loss landscape of neural nets.

Port-Hamiltonian Approach to Neural Network Training Visualizing the loss landscape of neural nets

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.324940Z

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Observation e3654960-4386-4a19-b728-5c0d441cad8f · outbound

This paper cites Training a 3-node neural network is np-complete.

Port-Hamiltonian Approach to Neural Network Training Training a 3-node neural network is np-complete

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.310303Z

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Observation 2f8efb5a-405e-4166-87dd-65c287a15deb · outbound

This paper cites Port-controlled hamiltonian systems: modelling origins and systemtheoretic properties.

Port-Hamiltonian Approach to Neural Network Training Port-controlled hamiltonian systems: modelling origins and systemtheoretic properties

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.298324Z

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Observation ab5e4a70-d318-45e4-87e4-f688abb190f8 · outbound

This paper cites Modeling and control of complex physical systems: the port-Hamiltonian approach.

Port-Hamiltonian Approach to Neural Network Training Modeling and control of complex physical systems: the port-Hamiltonian approach

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.286537Z

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Observation 15ecdc65-7668-4371-be66-4b3e06ed37c6 · outbound

This paper cites Port-hamiltonian systems theory: An introductory overview.

Port-Hamiltonian Approach to Neural Network Training Port-hamiltonian systems theory: An introductory overview

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.273624Z

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Observation ebcadb9a-2259-46ef-af4d-a79e6544655a · outbound

This paper cites Putting energy back in control.

Port-Hamiltonian Approach to Neural Network Training Putting energy back in control

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.261628Z

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Observation 9c7c2596-f8ba-4175-8f16-7e0590a8bcb2 · outbound

This paper cites Interconnection and damping assignment passivity- based control of port-controlled hamiltonian systems.

Port-Hamiltonian Approach to Neural Network Training Interconnection and damping assignment passivity- based control of port-controlled hamiltonian systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.249307Z

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Observation 787c8971-7375-4939-b708-50bcd6093e74 · outbound

This paper cites Control by interconnection and standard passivity- based control of port-hamiltonian systems.

Port-Hamiltonian Approach to Neural Network Training Control by interconnection and standard passivity- based control of port-hamiltonian systems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.235737Z

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Observation f6bf3f5b-57a0-4dc6-b577-24a6c2d9d04c · outbound

This paper cites Neural ordinary differential equations.

Port-Hamiltonian Approach to Neural Network Training Neural ordinary differential equations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.223326Z

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Observation 2467a84b-c1f8-4f25-b21f-9be690e58a0a · outbound

This paper cites Deep Neural Networks Motivated by Partial Differential Equations.

Port-Hamiltonian Approach to Neural Network Training Deep Neural Networks Motivated by Partial Differential Equations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.899404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6b11d80-b7f9-40f0-afec-a4a94a9046a6 · outbound

This paper cites Hamiltonian Neural Networks.

Port-Hamiltonian Approach to Neural Network Training Hamiltonian Neural Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T04:48:03.903836Z

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Unavailable: canonical work link unavailable.

Observation 14152578-394b-4324-a83f-833d1fdd1f40 · outbound

This paper cites Deep relaxation: partial differential equations for optimizing deep neural networks.

Port-Hamiltonian Approach to Neural Network Training Deep relaxation: partial differential equations for optimizing deep neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.210647Z

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Observation 775e2a6b-1aa7-4ffb-9b75-0f6c40c98348 · outbound

This paper cites Gradient and hamiltonian dynamics applied to learning in neural networks.

Port-Hamiltonian Approach to Neural Network Training Gradient and hamiltonian dynamics applied to learning in neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.199117Z

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Observation 0b0683a3-179f-4d37-85b5-55748199f7b9 · outbound

This paper cites Learning and system modeling via hamiltonian neural networks.

Port-Hamiltonian Approach to Neural Network Training Learning and system modeling via hamiltonian neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.186974Z

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Observation 01cd9a6c-19bc-4e3a-8979-5647be4da458 · outbound

This paper cites A learning algorithm for boltzmann machines.

Port-Hamiltonian Approach to Neural Network Training A learning algorithm for boltzmann machines

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.174730Z

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

Observation 99c6b27d-30b8-4880-a471-a806b0ea382d · outbound

This paper cites Neural networks and physical systems with emer- gent collective computational abilities.

Port-Hamiltonian Approach to Neural Network Training Neural networks and physical systems with emer- gent collective computational abilities

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.162241Z

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

Observation 9b9c3ca7-b2de-438f-b043-d4b47ea6d06b · outbound

This paper cites Theory of holors: A generalization of tensors.

Port-Hamiltonian Approach to Neural Network Training Theory of holors: A generalization of tensors

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.148940Z

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

Observation f9cb90e4-941a-4a82-9fb3-e69121dc929b · outbound

This paper cites Tikhonov regularization and total least squares.

Port-Hamiltonian Approach to Neural Network Training Tikhonov regularization and total least squares

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.135366Z

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Observation 1eef4c8e-b3d2-43b1-93a0-c220f864ac69 · outbound

This paper cites A simple weight decay can improve generalization.

Port-Hamiltonian Approach to Neural Network Training A simple weight decay can improve generalization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.122499Z

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

Observation 54e9d7f6-7434-4035-95f7-63f558d20923 · outbound

This paper cites Multistable energy shaping of linear time–invariant systems with hybrid mode selector.

Port-Hamiltonian Approach to Neural Network Training Multistable energy shaping of linear time–invariant systems with hybrid mode selector

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.108436Z

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Observation 3919703a-21f0-4559-8ca6-b6a84fee7186 · outbound

This paper cites An introduction to hybrid dynamical systems , volume 251.

Port-Hamiltonian Approach to Neural Network Training An introduction to hybrid dynamical systems , volume 251

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.095676Z

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Observation 9db573b3-1673-46bc-9734-3e6e96130a66 · outbound

This paper cites The Dufﬁng equation: nonlin- ear oscillators and their behaviour.

Port-Hamiltonian Approach to Neural Network Training The Dufﬁng equation: nonlin- ear oscillators and their behaviour

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.082874Z

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Observation 6245840b-6ea3-4752-8c17-27f019cf5236 · outbound

This paper cites The expressive power of neural networks: A view from the width.

Port-Hamiltonian Approach to Neural Network Training The expressive power of neural networks: A view from the width

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.070442Z

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Observation c3c13ff3-6d64-49c6-8a50-1b9d01c654ed · outbound

This paper cites The power of depth for feedforward neural networks.

Port-Hamiltonian Approach to Neural Network Training The power of depth for feedforward neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:48:04.057574Z

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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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