Paper Citation Record · LEDGER

Deep Learning for Individual Heterogeneity

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2010.14694.

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

pith.paper-citation-record.v1
2010.14694 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:57:57.466950Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T16:47:09.294913Z

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 b6c2b737-b219-43de-b436-4abb90c48371 · inbound

Estimating Parameters of Structural Models Using Neural Networks cites this paper.

Estimating Parameters of Structural Models Using Neural Networks Deep Learning for Individual Heterogeneity

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-08T20:57:57.466950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 320bf31f-f72c-443e-9ccc-f01cc2033c54 · inbound

Enhancing the Merger Simulation Toolkit with ML/AI cites this paper.

Enhancing the Merger Simulation Toolkit with ML/AI Deep Learning for Individual Heterogeneity

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:31:20.730747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab7900f9-0418-4acd-931e-173294417ba9 · inbound

Decoding Consumer Preferences Using Attention-Based Language Models cites this paper.

Decoding Consumer Preferences Using Attention-Based Language Models Deep Learning for Individual Heterogeneity

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.050153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a4307b2-9cb4-4c30-bc9d-965552a325da · inbound

Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments cites this paper.

Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments Deep Learning for Individual Heterogeneity

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T20:32:22.565899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b5d7369-977f-4cfd-a822-435b434e1c9e · inbound

Learning Preferences from Conjoint Data: A Hybrid Structural Deep Learning Approach cites this paper.

Learning Preferences from Conjoint Data: A Hybrid Structural Deep Learning Approach Deep Learning for Individual Heterogeneity

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-23T04:13:36.879609Z

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.

Observation 51cedbc1-c148-47b0-b196-9b3be5e053d4 · inbound

Learning Preferences from Conjoint Data: A Hybrid Structural Deep Learning Approach cites this paper.

Learning Preferences from Conjoint Data: A Hybrid Structural Deep Learning Approach Deep Learning for Individual Heterogeneity

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T22:18:36.409728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bbcb6ff-635f-4045-9bd2-4b125cb916cb · inbound

Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach cites this paper.

Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach Deep Learning for Individual Heterogeneity

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T16:47:09.296828Z

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.