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Paper Citation Record · LEDGER

Block Neural Autoregressive Flow

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1904.04676.

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

pith.paper-citation-record.v1
1904.04676 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:28:24.258572Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T06:05:35.184333Z

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 2a215ab5-35c3-46b0-8dbe-1508698a1191 · inbound

Unconstrained Monotonic Neural Networks cites this paper.

Unconstrained Monotonic Neural Networks Block Neural Autoregressive Flow

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-14T13:28:24.258572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:28:24.258572Z digest=sha256:40e49a57dd096a8dc217707384ec3c6d5f95ea3b420116e096787f6a4ae72317

Observation 84be7c76-8790-4121-9950-1c999d1881e5 · inbound

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport cites this paper.

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport Block Neural Autoregressive Flow

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-12T17:23:17.968695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:23:17.968695Z digest=sha256:9045c248e378bf38b1a56e2e111c7958e264169edbbbdabd769b2aa8d2c55b7f

Observation 33ba6773-3cb0-4f3c-8881-99f7d046089c · inbound

Morphological and Star Formation Properties of Cosmic Noon Massive Quiescent Galaxies cites this paper.

Morphological and Star Formation Properties of Cosmic Noon Massive Quiescent Galaxies Block Neural Autoregressive Flow

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-07-04T23:32:31.031979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-08T19:01:57.066359Z digest=sha256:d41e2870db13392b30e4005a5f17d131bcf6b37a035bfdf3615e6e4ec8f37b49