Paper Citation Record · LEDGER

Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2110.01765.

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

pith.paper-citation-record.v1
2110.01765 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:57:25.368723Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T11:46:55.373311Z

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 a5e60086-e8bc-49fe-9be1-33352f63dc8e · inbound

The Exploration of Neural Collapse under Imbalanced Data cites this paper.

The Exploration of Neural Collapse under Imbalanced Data Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T12:27:32.937404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87eaef0f-2c37-4517-9ff1-f59adef74679 · inbound

Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ? cites this paper.

Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ? Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T20:07:55.472459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59b06326-5916-47b7-9a17-d49c8b382d79 · inbound

Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer cites this paper.

Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:57:22.750524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a3a4d9c-8f6d-40e1-8689-16320b633efc · inbound

ResNets Are Deeper Than You Think cites this paper.

ResNets Are Deeper Than You Think Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:57:25.368723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae1f7a83-b59a-4542-b00b-825e12eee356 · inbound

Gated Normalization Removal and Scale Anchoring in Pre-Norm Transformers cites this paper.

Gated Normalization Removal and Scale Anchoring in Pre-Norm Transformers Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:20:13.530977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5d4d470e-6ace-47ed-a2fc-273e62031fc9 · inbound

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory cites this paper.

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:46:55.375201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.