Paper Citation Record · LEDGER

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.01581.

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

pith.paper-citation-record.v1
2406.01581 v2

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:40.617364Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:16:44.862552Z

Reference resolution

0 of 0 outbound references displayed

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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 4a46ccb1-c848-4c3b-ae50-394da2df9736 · inbound

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning cites this paper.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.617364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 048d95b9-6d3e-4161-a529-51d4dcb91ca0 · inbound

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions cites this paper.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T15:34:16.709105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ac3e478-bbe8-4e5c-94d3-040fe6c75369 · inbound

Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression cites this paper.

Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T22:45:01.842552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04466d28-ac93-4a25-9643-a50a0d8111c9 · inbound

Limitations of SGD for Multi-Index Models Beyond Statistical Queries cites this paper.

Limitations of SGD for Multi-Index Models Beyond Statistical Queries Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T04:19:45.403757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81736fd2-a0ce-485c-a613-435705b9efb8 · inbound

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:31:24.303352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aba5695e-beb9-4e0d-85fa-025b9a9eb587 · inbound

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks cites this paper.

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:16:44.864272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.