Paper Citation Record · LEDGER

Robust Convolution Neural ODEs via Contractivity-promoting regularization

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2508.11432.

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

pith.paper-citation-record.v1
2508.11432 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:02:25.110047Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

40 of 40 outbound references displayed

verified exact4
verified fuzzy29
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e9b60b6b-9776-41fb-8f75-64de87dd2b56 · outbound

This paper cites Adversarial attacks and defenses in images, graphs and text: A review,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Adversarial attacks and defenses in images, graphs and text: A review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.333106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 144c5f66-214f-4887-a3da-7e9d12739221 · outbound

This paper cites Intriguing properties of neural networks.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Intriguing properties of neural networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T20:02:21.947335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ad02be7-6a21-41d1-bcb6-9d5ae0a1dc9e · outbound

This paper cites Adversarial learning target- ing deep neural network classification: A comprehensive review of defenses against attacks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Adversarial learning target- ing deep neural network classification: A comprehensive review of defenses against attacks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.323858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d36861e0-b4fa-4d5a-ae20-96c3de2c20aa · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Explaining and Harnessing Adversarial Examples

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T20:02:22.219363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc37a61-0dac-4eaf-b014-e16ae8654a9a · outbound

This paper cites Feature purification: How adversarial training performs robust deep learning,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Feature purification: How adversarial training performs robust deep learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.315298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 102d7502-10ed-4422-8e3f-70394bb32485 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.306505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d6e4dd49-a41d-4ae9-8cc2-623967f83e30 · outbound

This paper cites Training robust neural networks using Lipschitz bounds,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Training robust neural networks using Lipschitz bounds,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T20:02:22.576661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bac6d8a-8eda-45dd-9740-89a02be2ad9f · outbound

This paper cites Robust- ness against adversarial attacks in neural networks using incremental dissipativity,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robust- ness against adversarial attacks in neural networks using incremental dissipativity,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.293447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8fcd4afd-f0ab-47b1-9b9f-69a5635b6acf · outbound

This paper cites Stable architectures for deep neural networks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Stable architectures for deep neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.285033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ac9a2a7b-ff75-4573-86cb-ee09e1d70d2a · outbound

This paper cites Neural ordinary differential equations,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Neural ordinary differential equations,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.277723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 357a2050-49b2-4156-a97a-660dca30640d · outbound

This paper cites Latent ordinary differential equations for irregularly-sampled time series,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Latent ordinary differential equations for irregularly-sampled time series,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.270573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a5774c36-b6fa-4e84-b970-79a7ea7fbf51 · outbound

This paper cites Hamiltonian neural networks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Hamiltonian neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.262848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e6aaf373-6991-456a-8bef-edc459d71ca5 · outbound

This paper cites Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.255444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14eaae92-b69e-429f-b9d1-36896e1b9b0a · outbound

This paper cites Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-05T20:02:25.989127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e20da19c-637c-49de-8ad0-4204e3c5ef97 · outbound

This paper cites On contraction analysis for non- linear systems,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization On contraction analysis for non- linear systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.247905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 402c5365-05e4-4770-bce2-09af014e1824 · outbound

This paper cites Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.239968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12c892c6-71d1-4724-a23f-2112269aa3be · outbound

This paper cites Towards Robust Neural Networks via Close-loop Control.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Towards Robust Neural Networks via Close-loop Control

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T20:02:25.730098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4744b7e4-1c92-4d9a-9207-21a54bd1104b · outbound

This paper cites On robustness of neural ordinary differential equations,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization On robustness of neural ordinary differential equations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.232035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14ebc232-8e65-4244-9e3b-71f5355aa461 · outbound

This paper cites Stable neural ode with Lyapunov-stable equilibrium points for defending against adversarial attacks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Stable neural ode with Lyapunov-stable equilibrium points for defending against adversarial attacks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.223954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2a5c35b7-c1b5-4b1c-b8b1-028625f773b5 · outbound

This paper cites LyaNet: A Lyapunov framework for training neural ODEs,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization LyaNet: A Lyapunov framework for training neural ODEs,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.215455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 145d8dba-69a0-4f49-8280-e0985f908f76 · outbound

This paper cites Stable Neural Flows.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Stable Neural Flows

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T20:02:23.703148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7381417c-efa0-44fe-bde3-d85471698443 · outbound

This paper cites Robust implicit networks via non-Euclidean contractions,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robust implicit networks via non-Euclidean contractions,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.207059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7bb93bb0-f0d3-4c79-ba27-0f57dcae3c18 · outbound

This paper cites Contracting implicit recurrent neural networks: Stable models with improved trainability,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Contracting implicit recurrent neural networks: Stable models with improved trainability,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.198917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bd75887d-25ab-4720-b2e3-2ed979be6a1f · outbound

This paper cites Robustness certificates for implicit neural networks: A mixed mono- tone contractive approach,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robustness certificates for implicit neural networks: A mixed mono- tone contractive approach,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.190672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 842f7b1d-dddb-43f3-b40c-8a106293ed4f · outbound

This paper cites Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-05T20:02:25.476119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c192a7c1-7c1d-4491-83e2-fad3e3459723 · outbound

This paper cites Robust classification using contractive Hamiltonian neural ODEs,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robust classification using contractive Hamiltonian neural ODEs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.182407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1900db72-e6ef-42dd-aba0-a4166c351a42 · outbound

This paper cites Learning stabilizable nonlinear dynamics with contraction-based regularization,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Learning stabilizable nonlinear dynamics with contraction-based regularization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.174205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff3a03b1-d842-4b3d-bb37-4258d53972a1 · outbound

This paper cites Recurrent equilibrium networks: Unconstrained learning of stable and robust dynamical models,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Recurrent equilibrium networks: Unconstrained learning of stable and robust dynamical models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.166181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f36467c0-1df7-4e89-9816-5d3af391afc0 · outbound

This paper cites Neural Exponential Stabilization of Control-affine Nonlinear Systems.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Neural Exponential Stabilization of Control-affine Nonlinear Systems

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-05T20:02:25.287765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7ad6e0b-1bd6-4c27-b68a-ae13fb4f83cd · outbound

This paper cites Automatic differen- tiation in pytorch,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Automatic differen- tiation in pytorch,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.156437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6db9ba17-2759-4481-8140-ee63f7f3adfd · outbound

This paper cites Bullo, Contraction Theory for Dynamical Systems.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Bullo, Contraction Theory for Dynamical Systems

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T20:02:24.502031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74be08d6-6199-4391-9512-0bd9f21e70db · outbound

This paper cites Convergent systems vs. incremental stability,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Convergent systems vs. incremental stability,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.142012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 249b0b2b-23b1-48cb-bfab-8e19ccde2bee · outbound

This paper cites Ro- bustness may be at odds with accuracy,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Ro- bustness may be at odds with accuracy,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.132781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58618cac-7b39-40ea-96be-c62c0defe1ab · outbound

This paper cites an unresolved cited work.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-05T20:02:27.123864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9e90e1a0-d581-4690-bbef-16621d696328 · outbound

This paper cites Goodfellow, Y.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Goodfellow, Y

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:27.037918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 755be700-1cf0-4a39-9173-732f20acb0e1 · outbound

This paper cites Nais-net: Stable deep networks from non-autonomous differential equations,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Nais-net: Stable deep networks from non-autonomous differential equations,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:26.808863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a743e0f5-97c1-48eb-aad9-d5c1bc14c43a · outbound

This paper cites Towards the first ad- versarially robust neural network model on MNIST,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Towards the first ad- versarially robust neural network model on MNIST,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:26.588416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8cea8b61-0f3a-487b-a09c-ba6a46a78433 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T20:02:25.000975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f533b93-5ffe-4b7b-a85a-f0826e9a18d0 · outbound

This paper cites Adversarial robustness of stabilized neural ode might be from obfuscated gradients,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Adversarial robustness of stabilized neural ode might be from obfuscated gradients,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:26.411973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a4e3265d-4516-4b6c-a759-ce872034691a · outbound

This paper cites The weight γ for the regularization term (14) is set to 1.

Robust Convolution Neural ODEs via Contractivity-promoting regularization The weight γ for the regularization term (14) is set to 1

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:26.253196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.