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Paper Citation Record · LEDGER

Efficient Representation of Low-Dimensional Manifolds using Deep Networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1602.04723.

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

pith.paper-citation-record.v1
1602.04723 v1

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 2026-08-14T15:53:12.507171Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T13:22:00.345590Z

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 f8f3b6f2-8362-4775-b0c8-9390d9797ffa · inbound

Deep ReLU network approximation of functions on a manifold cites this paper.

Deep ReLU network approximation of functions on a manifold Efficient Representation of Low-Dimensional Manifolds using Deep Networks

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-14T15:53:12.507171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:53:12.507171Z digest=sha256:415399e3b3fb01b82809f1c68fb5308b39ce5de38713b0a45cdc9f96326a34b0

Observation 3d0ddd87-e67e-4eff-b578-8cc59f93c5fe · inbound

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces cites this paper.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Efficient Representation of Low-Dimensional Manifolds using Deep Networks

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-06T13:22:00.437498Z

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=arxiv_source observed=2026-08-06T13:21:58.932509Z digest=sha256:1ef73f3912c5e75a26eff16d81982b00e30fccc9f8dd103002029465427bcbf1