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Paper Citation Record · LEDGER

Bounded KRnet and its applications to density estimation and approximation

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.09063.

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

pith.paper-citation-record.v1
2305.09063 v4

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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:19:02.297827Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T22:52:13.343161Z

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 1923ac7f-40c6-48de-9d11-404ca78e6c2d · inbound

Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths cites this paper.

Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths Bounded KRnet and its applications to density estimation and approximation

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-10T14:19:02.297827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:19:02.297827Z digest=sha256:9d7ba470392cb3b4193ce7098317dbeed7859f70967d963a85eee384d5992b1b

Observation 2362cb37-9209-465b-a38b-7baf46f215f3 · inbound

Integral regularization PINNs for evolution equations cites this paper.

Integral regularization PINNs for evolution equations Bounded KRnet and its applications to density estimation and approximation

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-22T22:52:13.347367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-22T22:51:11.411751Z digest=sha256:7677604b68661aea23486c1e5cb052360c0b7ae6695077d21a75cdf806321ec3