Paper Citation Record · LEDGER

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:32:26.529259Z

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

verified exact0
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 80ea4658-9103-4cbf-a321-19bfcd2c7b8c · inbound

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution cites this paper.

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T19:32:26.529259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc779744-8424-470b-b98e-afe19be35b99 · inbound

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks cites this paper.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T12:47:08.042205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0b3c3a6-0c47-4358-a202-52f6c45d19a8 · inbound

Offline Stochastic Optimization of Black-Box Objective Functions cites this paper.

Offline Stochastic Optimization of Black-Box Objective Functions A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:54.969663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5b2b8e0-c4fb-4cb4-b295-8567443c7197 · inbound

Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks cites this paper.

Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T20:26:43.421051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 821a7bad-9ca0-4a91-be5d-768160597ff3 · inbound

Adaptive kernel predictors from feature-learning infinite limits of neural networks cites this paper.

Adaptive kernel predictors from feature-learning infinite limits of neural networks A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T11:19:06.821739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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.

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.