Paper Citation Record · LEDGER

Enhancing Symbolic Machine Learning by Subsymbolic Representations

As of 17 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.14569.

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

pith.paper-citation-record.v1
2506.14569 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:55:58.943374Z

measured 24 of 24 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy3
unresolved17
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 510b6baf-e1b6-445b-9d25-a14f48dd3c51 · outbound

This paper cites UCI Machine Learning Repository (2011).

Enhancing Symbolic Machine Learning by Subsymbolic Representations UCI Machine Learning Repository (2011)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.818940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2739f90-6470-45e7-800e-af58e6a83e10 · outbound

This paper cites Artificial Intelligence 303, 103649 (2022).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Artificial Intelligence 303, 103649 (2022)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.826951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a994c6f-3d7f-4a34-8007-20983db77b61 · outbound

This paper cites Artificial intelligence 101(1-2), 285–297 (1998).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Artificial intelligence 101(1-2), 285–297 (1998)

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-15T19:55:58.832375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22939fab-ba52-4075-aa3e-c89128e61daf · outbound

This paper cites bioRxiv pp.

Enhancing Symbolic Machine Learning by Subsymbolic Representations bioRxiv pp

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.837639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9435d4c-e467-4b38-b67d-ad73cb88fa5b · outbound

This paper cites an unresolved cited work.

Enhancing Symbolic Machine Learning by Subsymbolic Representations Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.844639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35d09217-d015-4f0a-b4f5-3af0e2b454ff · outbound

This paper cites https://huggingface.co/fse/glove-twitter-200 (2023), accessed: 2025-05-28.

Enhancing Symbolic Machine Learning by Subsymbolic Representations https://huggingface.co/fse/glove-twitter-200 (2023), accessed: 2025-05-28

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:55:59.581159Z

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.

Observation 8ad1de51-f33f-4858-8cd7-5c1e8edc3cf0 · outbound

This paper cites Distilling a Neural Network Into a Soft Decision Tree.

Enhancing Symbolic Machine Learning by Subsymbolic Representations Distilling a Neural Network Into a Soft Decision Tree

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.856431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfae321d-1a6f-46f5-8608-2a5bb6ce800c · outbound

This paper cites Neurosymbolic AI and its Taxonomy: a survey.

Enhancing Symbolic Machine Learning by Subsymbolic Representations Neurosymbolic AI and its Taxonomy: a survey

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.861732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b9364a9-9c97-4fd5-84ad-33ba7de4b655 · outbound

This paper cites The Tree Ensemble Layer: Differentiability meets Conditional Computation.

Enhancing Symbolic Machine Learning by Subsymbolic Representations The Tree Ensemble Layer: Differentiability meets Conditional Computation

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T19:55:59.125392Z

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.

Observation 9c7ae287-3dec-499f-8896-43d691f105ef · outbound

This paper cites Cell166(3), 740–754 (2016).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Cell166(3), 740–754 (2016)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:55:59.565592Z

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.

Observation 4074bcaf-8ba8-40c9-a3d8-1c1c31e7b53c · outbound

This paper cites Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning.

Enhancing Symbolic Machine Learning by Subsymbolic Representations Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.878178Z

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Unavailable: canonical work link unavailable.

Observation 7d97c70a-7637-4abe-b2c8-7bf61bacc24d · outbound

This paper cites In: 2011 IEEE 11th international conference on data mining.

Enhancing Symbolic Machine Learning by Subsymbolic Representations In: 2011 IEEE 11th international conference on data mining

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.883173Z

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Unavailable: canonical work link unavailable.

Observation 66415dbf-b262-4da3-b874-169d86a6c433 · outbound

This paper cites In: Pro- ceedings of the 22nd international conference on Machine learning.

Enhancing Symbolic Machine Learning by Subsymbolic Representations In: Pro- ceedings of the 22nd international conference on Machine learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.889300Z

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Unavailable: canonical work link unavailable.

Observation c8d5d260-89d0-47f7-8e94-35894872d374 · outbound

This paper cites In: Kambhampati, S.

Enhancing Symbolic Machine Learning by Subsymbolic Representations In: Kambhampati, S

Reference 14

Resolution: verified exact; doi, observed 2026-08-15T19:55:59.078714Z

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.

Observation 07d1db9c-85c4-464e-b325-3278afd8d161 · outbound

This paper cites DeepProbLog: Neural Probabilistic Logic Programming.

Enhancing Symbolic Machine Learning by Subsymbolic Representations DeepProbLog: Neural Probabilistic Logic Programming

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.899909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21f2c1bc-51f3-453c-b9a7-df81011b0d72 · outbound

This paper cites The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision.

Enhancing Symbolic Machine Learning by Subsymbolic Representations The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.905328Z

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Unavailable: canonical work link unavailable.

Observation f4fe4749-903c-47cb-8f7d-ee87286ebfa2 · outbound

This paper cites Artificial Intelligence328, 104062 (2024).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Artificial Intelligence328, 104062 (2024)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.910588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fd2d64a-a433-4637-bd44-6981253756e1 · outbound

This paper cites In: Proceedings of the 24th international conference on Machine learning.

Enhancing Symbolic Machine Learning by Subsymbolic Representations In: Proceedings of the 24th international conference on Machine learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.914824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4811ac00-eec2-4663-aa8d-4b6e1edaf2ed · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Enhancing Symbolic Machine Learning by Subsymbolic Representations Efficient Estimation of Word Representations in Vector Space

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.919061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48e92196-5ae7-495f-b1d9-4d3c2e7199da · outbound

This paper cites In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP).

Enhancing Symbolic Machine Learning by Subsymbolic Representations In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.924713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c5e14d7-f5a6-4e42-83a1-8b4ff30a291e · outbound

This paper cites Machine learning62, 107– 136 (2006).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Machine learning62, 107– 136 (2006)

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.929440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80dc6086-752c-4342-9231-e934b39c767c · outbound

This paper cites Bioinformatics 35(14), i501–i509 (2019).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Bioinformatics 35(14), i501–i509 (2019)

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T19:55:58.933842Z

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Observation 86b39e0b-43f4-4c02-966e-b24a01d37bbd · outbound

This paper cites Roth et al.

Enhancing Symbolic Machine Learning by Subsymbolic Representations Roth et al

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:55:59.550478Z

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.

Observation 42aa5ab8-8d3c-4da5-af37-c0d81cbf5521 · outbound

This paper cites Neural Networks 166, 105–126 (2023).

Enhancing Symbolic Machine Learning by Subsymbolic Representations Neural Networks 166, 105–126 (2023)

Reference 24

Resolution: verified exact; doi, observed 2026-08-15T19:55:58.981011Z

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.

Pith citing papers

No inbound Pith citation observations are available.