Paper Citation Record · LEDGER

Learning Interpretable Differentiable Logic Networks for Time-Series Classification

As of 10 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2508.17512.

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

pith.paper-citation-record.v1
2508.17512 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:56:19.822615Z

measured 43 of 43 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 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

43 of 43 outbound references displayed

verified exact0
verified fuzzy30
unresolved13
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f4921e9d-9eb5-4c3c-a9f3-9605d59b3a94 · outbound

This paper cites Petersen, C.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Petersen, C

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:31.159588Z

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Observation 50b03c4d-1bb4-497b-9aa5-93a1e654bc51 · outbound

This paper cites Petersen, H.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Petersen, H

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:30.888508Z

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Observation 97c146ae-6ee8-40cb-991b-48e3877050ef · outbound

This paper cites Yue and N.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Yue and N

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:30.595002Z

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Observation bb940fd7-0dd4-4748-9f56-87251ef140d2 · outbound

This paper cites Learning Interpretable Differentiable Logic Networks for Tabular Regression.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Learning Interpretable Differentiable Logic Networks for Tabular Regression

Reference 4

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Source-reported events for the cited work

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Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 5

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Observation 297180e4-6bbc-4399-9d77-41d1772145e8 · outbound

This paper cites Christ, N.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Christ, N

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:29.950913Z

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Observation f67669da-977b-4220-818d-faee8b9a46c3 · outbound

This paper cites an unresolved cited work.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:56:29.545947Z

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Observation 8405a278-2100-4795-abdd-87448dfd1483 · outbound

This paper cites an unresolved cited work.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 8

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Observation f0cc9dd3-2aaf-4f77-9e18-81bf6fa13508 · outbound

This paper cites an unresolved cited work.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 9

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Observation 16fc017b-5985-4078-8e10-841adaedffbf · outbound

This paper cites Menger, ``Statistical metrics,'' Selecta Math., vol.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Menger, ``Statistical metrics,'' Selecta Math., vol

Reference 10

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Observation bc503226-eda0-4c60-a1f7-27f14257038b · outbound

This paper cites Rudin, ``Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,'' Nat.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Rudin, ``Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,'' Nat

Reference 11

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Observation c2a4aa55-dd66-4077-bed3-18f7b8d2cc84 · outbound

This paper cites Why should I trust you?.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Why should I trust you?

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:27.978328Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0d50c62-9521-41d0-9186-94e45147ab64 · outbound

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Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 13

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Observation 0884e76e-2715-4006-9422-958273c8eca3 · outbound

This paper cites an unresolved cited work.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 14

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Observation 06556867-bcd3-42fa-971e-615af75fc757 · outbound

This paper cites Wang and C.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Wang and C

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:27.251860Z

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Observation 55b8256d-5707-485f-b4e1-b7ed3cbc25f1 · outbound

This paper cites Rudin, C.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Rudin, C

Reference 16

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Source-reported events for the cited work

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Observation 9eed00e8-ccd1-49ed-9f63-afabdf6ca08a · outbound

This paper cites Umuroglu, Y.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Umuroglu, Y

Reference 17

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Source-reported events for the cited work

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Observation 6c68487c-99d5-4e84-ac6f-9bf4c00cc168 · outbound

This paper cites The UEA multivariate time series classification archive, 2018.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification The UEA multivariate time series classification archive, 2018

Reference 18

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Source-reported events for the cited work

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Observation 49e043f1-9287-4a88-9aea-9ec42ff0ca2e · outbound

This paper cites Middlehurst, P.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Middlehurst, P

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:26.463440Z

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 755e30b6-fc22-41c6-b8ac-7c56eb1f2ef1 · outbound

This paper cites Rakthanmanon, B.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Rakthanmanon, B

Reference 20

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Observation 7f98d635-b28c-4a65-8c90-9fc1f2af2f82 · outbound

This paper cites Middlehurst and A.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Middlehurst and A

Reference 21

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 68ca3299-1173-49e0-b4a2-0665bf83101d · outbound

This paper cites Dempster, D.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Dempster, D

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:25.402603Z

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Observation 6418cee7-abe6-4fec-9877-8d37f26c5205 · outbound

This paper cites Hills, J.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Hills, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:25.112286Z

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 21421076-d26d-4420-93ba-19d2e1ac9e73 · outbound

This paper cites Sch \"a fer, ``The BOSS is concerned with time series classification in the presence of noise,'' Data Min.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Sch \"a fer, ``The BOSS is concerned with time series classification in the presence of noise,'' Data Min

Reference 24

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d84cb514-6709-4e7c-af8e-5157d8b96fb8 · outbound

This paper cites a fer and M. H \.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification a fer and M. H \

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:24.496533Z

Source-reported events for the cited work

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Observation 62071265-174b-44b1-a187-8535970e3f8b · outbound

This paper cites Dempster, D.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Dempster, D

Reference 26

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Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c55a75d6-8d8b-4b57-a667-4398db99e642 · outbound

This paper cites Ismail-Fawaz, M.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Ismail-Fawaz, M

Reference 28

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Source-reported events for the cited work

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Observation 08b80014-57bd-488b-ad3c-baed0c5ab632 · outbound

This paper cites Middlehurst, J.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Middlehurst, J

Reference 29

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Observation a29cf4b5-2d3d-412c-985f-2267f5e16364 · outbound

This paper cites Guillaume, C.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Guillaume, C

Reference 30

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Observation d76c2d2c-5433-48c4-88bf-c37b1749c9e7 · outbound

This paper cites Sch \"a fer and U.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Sch \"a fer and U

Reference 31

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Observation ea649eea-348b-4eac-9120-a09f821eba62 · outbound

This paper cites Dempster, F.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Dempster, F

Reference 32

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Observation c2dd4c4e-cfaa-4d95-b237-d62841f2e45a · outbound

This paper cites Bagnall, J.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Bagnall, J

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:22.421669Z

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Observation e8ebf368-4abd-4649-8905-97ac68987a55 · outbound

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Learning Interpretable Differentiable Logic Networks for Time-Series Classification Unresolved cited work

Reference 34

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Observation 6716d0ed-ce40-4547-877d-a99305cd6865 · outbound

This paper cites Meurer, C.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Meurer, C

Reference 35

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Source-reported events for the cited work

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Observation 9740a640-6cb4-429f-95df-e82be8e8a78b · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:18.726327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f84f5e1-067e-436a-8a6a-adbc3b152e13 · outbound

This paper cites Cheng, L.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Cheng, L

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:21.747042Z

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 f4c2a3d1-a1fd-467f-88d5-8d5280ad9ae9 · outbound

This paper cites Akiba, S.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Akiba, S

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:21.421389Z

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 b085d296-e6b5-467a-8898-ce2cec3334d4 · outbound

This paper cites Pedregosa, G.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Pedregosa, G

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:21.104537Z

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 81d0e6d2-b68b-4037-81b5-75e65971a880 · outbound

This paper cites Paszke, S.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Paszke, S

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:20.826217Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 504d3ae7-7984-4aa7-8b13-900f9ab66f92 · outbound

This paper cites Middlehurst, A.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Middlehurst, A

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:20.542807Z

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 aeec094b-ae24-41a3-b281-52f1be701489 · outbound

This paper cites Moritz, R.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Moritz, R

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:20.276911Z

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 deb9d51a-8768-4867-9283-6cc43b81efb9 · outbound

This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Tune: A Research Platform for Distributed Model Selection and Training

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.