Paper Citation Record · LEDGER

SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2302.11055.

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

pith.paper-citation-record.v1
2302.11055 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:35:42.849130Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eb208633-a54a-4dbe-b5a0-c6fa7cffcaa3 · inbound

Position: A Theory of Deep Learning Must Include Compositional Sparsity cites this paper.

Position: A Theory of Deep Learning Must Include Compositional Sparsity SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:42.849130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1251d8b0-1494-4604-9e75-5d4e6328b0b9 · inbound

Specialization of softmax attention heads: insights from the high-dimensional single-location model cites this paper.

Specialization of softmax attention heads: insights from the high-dimensional single-location model SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T19:04:55.583204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8886210f-082c-4110-af98-023ae346682c · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:51:05.773020Z

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 152badbb-0171-4505-9261-a0419964ce1a · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:25:54.658739Z

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 b4877d7b-5747-4e2c-8928-5d4241e4b2f9 · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T00:45:12.044341Z

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 58fa1deb-52f8-45c1-95b3-a2c3783541e8 · inbound

The two clocks and the innovation window: When and how generative models learn rules cites this paper.

The two clocks and the innovation window: When and how generative models learn rules SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.051877Z

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 7e5c82b8-4c5e-49e6-a49c-e9efcf25bdd4 · inbound

Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases cites this paper.

Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:19:46.446291Z

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.