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Paper Citation Record · LEDGER

What can linearized neural networks actually say about generalization?

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.06770.

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

pith.paper-citation-record.v1
2106.06770 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:17:37.179898Z

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

14
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 385df943-c7d1-40ea-9711-e466110557f5 · inbound

Feature learning is decoupled from generalization in high capacity neural networks cites this paper.

Feature learning is decoupled from generalization in high capacity neural networks What can linearized neural networks actually say about generalization?

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T14:17:37.179898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:17:37.179898Z digest=sha256:7ea58a360bb539af693084b8982c7bf11ea3431fa5d8f5d3c4583c5b399c3aca

Observation eea912fe-bd1b-46da-994b-6dff2f8bc5fc · 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 What can linearized neural networks actually say about generalization?

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:31:22.846762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-12T05:27:11.761971Z digest=sha256:b4f40238336dc8e878076578b60ab5423d9860176a765aa64ba0020ba9d47ccc