Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2007.06007.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T03:21:49.777198Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T17:14:57.097242Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 57285470-8ce0-4c50-8522-9303ed829c56 · inbound
Control, Optimal Transport and Neural Differential Equations in Supervised Learning cites this paper.
Control, Optimal Transport and Neural Differential Equations in Supervised Learning Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Reference 74
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T23:52:16.961219Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 077950f5-1ab5-465d-bdee-dc0ab512dab6 · inbound
Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees cites this paper.
Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Reference 157
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T03:21:49.777198Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eae4d572-a925-4937-b6b1-278cbf158237 · inbound
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Reference 170
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T07:31:13.966330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9567071e-93c8-4892-8686-25df45f8c6c5 · inbound
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Reference 74
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T17:14:57.098663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 86726da7-7532-4742-b3a2-e0aef6817d2e · inbound
Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One cites this paper.
Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T16:40:19.220722Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 96acae49-ca29-42a7-861b-93da9975fca1 · inbound
Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One cites this paper.
Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T16:20:15.771616Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.