Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1806.05393.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:14:10.280183Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T16:26:06.009499Z
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 2cb9baf3-b721-4dda-bd33-a083a03220ae · inbound
Approximate Message Passing for Bayesian Neural Networks cites this paper.
Approximate Message Passing for Bayesian Neural Networks Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:14:10.280183Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34ee1b45-73af-41cc-938c-14da27e6b2e6 · inbound
A Spin Glass Characterization of Neural Networks cites this paper.
A Spin Glass Characterization of Neural Networks Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:10:36.916952Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2768518c-5bb8-462c-bf70-c2b31e2dba91 · inbound
Detecting Adversarial Data via Provable Adversarial Noise Amplification cites this paper.
Detecting Adversarial Data via Provable Adversarial Noise Amplification Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T22:54:04.084979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.