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Paper Citation Record · LEDGER

Directional convergence and alignment in deep learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2006.06657.

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

pith.paper-citation-record.v1
2006.06657 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:21:41.246338Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T16:21:41.396851Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • 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 cceef7bc-b387-4e24-83bb-c5b783ebe708 · inbound

On Generalization Bounds for Neural Networks with Low Rank Layers cites this paper.

On Generalization Bounds for Neural Networks with Low Rank Layers Directional convergence and alignment in deep learning

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-12T16:21:41.402068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T16:21:41.246338Z digest=sha256:349b209340d24ebb9f1193f36aa4060553cb4dbb8478d87e2f62a1223f9bef02

Observation f8c6f5ff-bdb3-4257-a3a0-e0016c6a2478 · inbound

Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer cites this paper.

Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer Directional convergence and alignment in deep learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-03T23:19:41.129479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:19:41.129479Z digest=sha256:9b9ef4889cc51f786feff1a1ca81ab438df7acb0cde3ab86f0169a7f5a5063e2