Paper Citation Record · LEDGER

Benefits of depth in neural networks

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1602.04485.

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

pith.paper-citation-record.v1
1602.04485 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:37:56.552559Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T00:52:14.254349Z

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 4b2127da-97ec-4295-bbfe-d755e5174519 · inbound

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence cites this paper.

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence Benefits of depth in neural networks

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:52:14.260841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b66e8934-1c72-4443-b9f7-cfc4527a0202 · inbound

Discrete Functional Geometry of ReLU Networks via ReLU Transition Graphs cites this paper.

Discrete Functional Geometry of ReLU Networks via ReLU Transition Graphs Benefits of depth in neural networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T16:37:56.552559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c77abd5e-840c-4d54-8acc-dd06da32d454 · inbound

On Universality of Deep Equivariant Networks cites this paper.

On Universality of Deep Equivariant Networks Benefits of depth in neural networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T09:26:10.426081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efb703b4-9d4e-4776-ad9f-117dbb85d85e · inbound

Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations cites this paper.

Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations Benefits of depth in neural networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T03:55:35.331715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.