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Paper Citation Record · LEDGER

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.04825.

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

pith.paper-citation-record.v1
1912.04825 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 2026-08-15T16:37:34.380811Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T09:21:20.936587Z

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 048059f8-54f9-4af4-b8f5-964f6e215eef · inbound

Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models cites this paper.

Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-15T16:37:34.380811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:37:34.380811Z digest=sha256:7ee61bcbc73c16aec627162113010fbb781e98c0b94e7616bf085a5173f0da3d

Observation 464c7243-e6fe-4287-83bd-ccab015e214a · inbound

Predicting intermediate-mass black hole formation in star clusters with machine learning cites this paper.

Predicting intermediate-mass black hole formation in star clusters with machine learning Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

Reference 85

Resolution
verified exact
arxiv_id, observed 2026-05-22T09:21:20.939587Z

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=2026-05-22T09:20:09.976842Z digest=sha256:a51dec255a5f6e1cb83e186e5cbd0624efcf1563b5bbde4a745ba4fb1e438868