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Paper Citation Record · LEDGER

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1810.02281.

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

pith.paper-citation-record.v1
1810.02281 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:34:47.155168Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:28:55.691977Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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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 556a6891-bb41-464b-97e6-d99aa37af2a7 · inbound

Intrinsic Strain-Driven Topological Evolution in SrRuO3 via Flexural Strain Engineering cites this paper.

Intrinsic Strain-Driven Topological Evolution in SrRuO3 via Flexural Strain Engineering A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T17:34:47.155168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:34:47.155168Z digest=sha256:425ec91f0d97a78b68f64820cbc9a412d3c74d64c90b2a0ec5d17ec26db399b2

Observation f7ad0784-5859-42c2-8174-5ccb4cfee4c0 · inbound

Geodesics in the Deep Linear Network cites this paper.

Geodesics in the Deep Linear Network A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-18T16:51:38.204481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-18T16:48:58.694276Z digest=sha256:240e062195854608f0ddb45ae38dedf55128c5a78048ce7f4401cc6b6b7f56a2

Observation 053a4bbb-6c8e-4b8c-9d3a-3a594ce5c65d · inbound

EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models cites this paper.

EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:41:01.982909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:53:51.162967Z digest=sha256:d46d8543198938036c804d77462585657ab030c6a0e268dd7d5516c1ebbc81a9

Observation 8f5047d6-6ab9-412d-a1cd-d3f072b02b71 · inbound

EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models cites this paper.

EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:17:28.675255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-13T07:16:15.202466Z digest=sha256:309b8bdd57aa05f33a0e833cdbad86cd4e7790c7aeaf8412baa438ee0e367aa8

Observation f7aad59b-0808-4ea6-8266-932c849b1a75 · inbound

The Implicit Bias of Depth: From Neural Collapse to Softmax Codes cites this paper.

The Implicit Bias of Depth: From Neural Collapse to Softmax Codes A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:26:38.876950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-25T05:26:15.556205Z digest=sha256:2f744ceb7aa286eddb190c29f79324ead54fb555d488d8269b062f92e8ede7c9

Observation 0854d5c6-788d-464e-9bbf-cab7aa89dd91 · inbound

Conservation Laws for Modern Neural Architectures cites this paper.

Conservation Laws for Modern Neural Architectures A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T20:08:55.235673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-27T01:43:45.287360Z digest=sha256:d701bd8de6204579fb64227e5f0cfcf262540be71f947cda76ceffc2fd8a66d7

Observation 884a32d0-aa9f-499c-97dc-876e8ff502b2 · inbound

Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias cites this paper.

Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-03T20:28:55.693580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T01:19:36.892842Z digest=sha256:782c12dc41133452be0dd73532dc1b4478e82d7a44bfde1da991566d537a9858

Observation 8f421669-c9b0-4bb9-96e3-7a41559a4b0c · inbound

How are linear representations learned? Exact solutions to the dynamics of abstraction cites this paper.

How are linear representations learned? Exact solutions to the dynamics of abstraction A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-07-13T06:19:30.027337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T06:19:30.027337Z digest=sha256:b7f2508f2e1b2e4a2d55f2695184f32bdbfdd16022e6685c46961a1e75b7aace

Observation c3025be4-ec25-4bc6-842e-3d5785ca882a · inbound

Differentiable Approximations for Distance Queries cites this paper.

Differentiable Approximations for Distance Queries A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-03T01:34:29.562169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T01:34:29.562169Z digest=sha256:69b26fb09f41388d61d5f6122e17ac950f1bbdf2314f7ac4f91530e949991702