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Paper Citation Record · LEDGER

How many degrees of freedom do we need to train deep networks: a loss landscape perspective

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.05802.

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

pith.paper-citation-record.v1
2107.05802 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:15:00.329382Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:44:37.263502Z

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 d9110df9-ad4f-4915-bf96-666ad9c581c8 · inbound

Gradient Weight-normalized Low-rank Projection for Efficient LLM Training cites this paper.

Gradient Weight-normalized Low-rank Projection for Efficient LLM Training How many degrees of freedom do we need to train deep networks: a loss landscape perspective

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T00:15:00.329382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:15:00.329382Z digest=sha256:22dc054b81157726d3ecd98c78e93a5d7497496624bd79103e24867189c68d03

Observation 45931957-3294-4814-9ff7-98b9280b7feb · inbound

Why Do More Experts Fail? A Theoretical Analysis of Model Merging cites this paper.

Why Do More Experts Fail? A Theoretical Analysis of Model Merging How many degrees of freedom do we need to train deep networks: a loss landscape perspective

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:44:37.342378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T13:44:33.847753Z digest=sha256:615970f36f86e147b8781d6d9ee3f76c5ef6a99422bb15d1bc55fd8d5b3f3742