Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
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 5 inbound Pith citation observations for arXiv:2406.01581.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T15:34:16.709105Z
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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- 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 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.