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Paper Citation Record · LEDGER

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs

As of 11 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2507.14874.

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

pith.paper-citation-record.v1
2507.14874 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T03:47:34.119935Z

measured 44 of 44 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T10:45:22.019824Z

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 exact7
  • verified fuzzy35
  • unresolved0
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3dd6c228-c7b3-4f21-8c8b-32f08a05d0b5 · outbound

This paper cites Interpretable rule-based architecture for gnss jamming signal classification.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Interpretable rule-based architecture for gnss jamming signal classification

Reference 1

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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.

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Observation 909c2ab6-06e7-42e2-9857-ea04952a95d4 · outbound

This paper cites Using Tsetlin Machine to discover interpretable rules in natural language processing applications.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Using Tsetlin Machine to discover interpretable rules in natural language processing applications

Reference 2

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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.

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Observation a39840ad-8130-472a-a643-55d8f6cc0921 · outbound

This paper cites Expert Systems , author =.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Expert Systems , author =

Reference 3

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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.

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Observation 65a38f6d-26cf-494a-9c43-0c96c57723b5 · outbound

This paper cites Enhancing interpretable clauses semantically using pretrained word representation.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Enhancing interpretable clauses semantically using pretrained word representation

Reference 4

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raw_fallback, observed 2026-05-19T03:52:02.524249Z

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.

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Observation 019f08f7-37e5-463e-ab7a-8cfb5b652c1f · outbound

This paper cites Tsetlin machine embedding: Representing words using logical expressions.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Tsetlin machine embedding: Representing words using logical expressions

Reference 5

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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.

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Observation cefcb306-bd2b-474e-b4d4-ad6d7c00ea61 · outbound

This paper cites Kadhim, Paul F.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Kadhim, Paul F

Reference 6

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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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:744fa58f106f11b2e262cd8261340a63f955599e0493eea571dc986aa5a3a2b7

Observation bdf39a32-eeb3-4505-865a-b06ac5b68df0 · outbound

This paper cites Multimodal learning with graphs.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Multimodal learning with graphs

Reference 7

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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.

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Observation d6c8e77b-6306-4db8-9c78-829b299d8d41 · outbound

This paper cites The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic

Reference 8

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arxiv_id, observed 2026-05-19T03:52:01.618341Z

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.

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Observation 8909a89e-0696-4f19-ad86-8503e6c48ae9 · outbound

This paper cites The Convolutional Tsetlin Machine.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs The Convolutional Tsetlin Machine

Reference 9

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arxiv_id, observed 2026-05-19T03:52:01.628144Z

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.

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Observation 08a61f05-eb95-424c-ac4f-f78ab8395dcf · outbound

This paper cites Drop Clause: Enhanc- ing Performance, Robustness and Pattern Recognition Capabilities of the Tsetlin Machine.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Drop Clause: Enhanc- ing Performance, Robustness and Pattern Recognition Capabilities of the Tsetlin Machine

Reference 10

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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.

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Observation ae62d7a4-0424-428c-bab2-231d98b74b2d · outbound

This paper cites Darshana Abeyrathna, Ole-Christoffer Granmo, Xuan Zhang, Lei Jiao, and Morten Goodwin.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Darshana Abeyrathna, Ole-Christoffer Granmo, Xuan Zhang, Lei Jiao, and Morten Goodwin

Reference 11

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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.

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Observation e3b0548e-e06e-4c5c-ac9e-63c29a0a2c12 · outbound

This paper cites Tsetlin machine for solving contextual bandit problems.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Tsetlin machine for solving contextual bandit problems

Reference 12

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raw_fallback, observed 2026-05-19T03:52:02.508344Z

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.

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Observation f0e5abb9-a5ec-4a1f-a4e6-e7f6b7927274 · outbound

This paper cites Rachkovskij.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Rachkovskij

Reference 13

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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.

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Observation 05455fad-7cd1-428e-a3b7-a8acff3c5fb2 · outbound

This paper cites Coalesced Multi-Output Tsetlin Machines with Clause Sharing.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Coalesced Multi-Output Tsetlin Machines with Clause Sharing

Reference 14

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arxiv_id, observed 2026-05-19T03:52:01.624751Z

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

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Observation 62cc7f70-8569-4122-a83b-2008e0b18f18 · outbound

This paper cites Gradient-based learning applied to document recognition.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Gradient-based learning applied to document recognition

Reference 15

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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.

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Observation f3be65db-a24c-4c1d-a3cc-e77e780b77c3 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 16

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local_arxiv, observed 2026-05-19T03:52:01.614420Z

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

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Observation efadaa24-da51-44bd-a3cf-f94230bf2e0b · outbound

This paper cites Learning multiple layers of features from tiny images.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Learning multiple layers of features from tiny images

Reference 17

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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.

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Observation 876cb985-afee-40ab-82a9-9ba3f4065e51 · outbound

This paper cites Smørvik, and Ole-Christoffer Granmo.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Smørvik, and Ole-Christoffer Granmo

Reference 18

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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.

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Observation 2787300b-311b-49d5-af5b-63211c1fdd49 · outbound

This paper cites Geometric deep learning on graphs and manifolds using mixture model cnns.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Geometric deep learning on graphs and manifolds using mixture model cnns

Reference 19

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raw_fallback, observed 2026-05-19T03:52:02.493166Z

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.

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Observation 0b900cae-56ad-4420-9706-fafb76bbc45f · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Convolutional neural networks on graphs with fast localized spectral filtering

Reference 20

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

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Observation 1db82c70-d269-4bb3-9849-9c0f27a7ed55 · outbound

This paper cites Maas, Raymond E.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Maas, Raymond E

Reference 21

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Observation 53786f7e-0226-4c29-889c-2888ada6bef0 · outbound

This paper cites Character-level convolutional networks for text classification.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Character-level convolutional networks for text classification

Reference 22

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

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Observation dc5dbbbf-c906-451d-b7df-c130f6917d7c · outbound

This paper cites Annotating Expressions of Opinions and Emotions in Language.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Annotating Expressions of Opinions and Emotions in Language

Reference 23

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

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Observation 1b0a397a-e13c-4a99-a116-349e59abd3bf · outbound

This paper cites Simpler Context-Dependent Logical Forms via Model Projections.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Simpler Context-Dependent Logical Forms via Model Projections

Reference 24

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local_arxiv, observed 2026-05-19T03:52:01.631142Z

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

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Observation 2ab8a3a4-e634-4411-b249-d80d217a9f9a · outbound

This paper cites From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

Reference 25

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local_arxiv, observed 2026-05-19T03:52:01.621547Z

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

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Observation 8e534b27-44d8-45b8-b5d3-1d3176c836dc · outbound

This paper cites Amazon sales dataset.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Amazon sales dataset

Reference 26

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raw_fallback, observed 2026-05-19T03:52:02.480326Z

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.

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Observation ff8925dc-565d-4794-84c2-e682aea84d03 · outbound

This paper cites Ncbi taxonomy: enhanced access via ncbi datasets.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Ncbi taxonomy: enhanced access via ncbi datasets

Reference 27

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raw_fallback, observed 2026-05-19T03:52:02.478022Z

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.

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Observation 0036b2a3-40f9-40d0-9991-9196474dde2a · outbound

This paper cites Include" is selected. The action becomes “Exclude.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Include" is selected. The action becomes “Exclude

Reference 28

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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.

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Observation c257cc8d-5ad9-4f25-bf04-d94d202f1be9 · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 29

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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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:615b6ca9282913f98c9848caeb2dd8baaa6db8782697cf52460a041f30a930f1

Observation 9edb11cb-f0a8-4330-89e5-57895a985528 · outbound

This paper cites Limitations.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Limitations

Reference 30

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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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:94c45560a5960908735019f1b8b824028f45b09bc3518b64c7daf3d77d9c2e85

Observation 24ee33c6-f3ac-4884-bfbd-611d62ad3dea · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 31

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raw_fallback, observed 2026-05-19T03:52:02.466292Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:bc1b7cc8d9245398b0d2859e64c14a4595d4a22239b45889962d58ffa2a7e1ba

Observation cd94fdbe-03cb-4066-971e-b61d7e599904 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not include experiments

Reference 32

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raw_fallback, observed 2026-05-19T03:52:58.042447Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:e7912669564b46910abedf9247b8d8b7f3c3f1daa7e498af606b7fdaa74b1593

Observation f8ae2b47-ef45-4fb8-86e3-ecdbfb74afb0 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 33

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raw_fallback, observed 2026-05-19T03:52:02.556604Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:dbdf987216c40b824e5ddacd8f2aa46388ff136a0d80bfedb36c7d7d53b57814

Observation d8d0572f-498e-4af3-9cef-4586431af436 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not include experiments

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.554211Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:f71932b64da1dcc54c1a8ee9d5b49af4e0c46383c8e707b09cac1348ee6d3db2

Observation 45eaff0d-00cd-42b5-b4bd-87fa9c27244b · outbound

This paper cites Experimental run wise details are added in supplemental material.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Experimental run wise details are added in supplemental material

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.551979Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:cf855667f99eb3381c151c0aaba47e2fa4ec6f5576ca39c409012a01e1664aab

Observation 7703d5ac-678d-4967-9ad8-1877a24b1827 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not include experiments

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.549694Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:f7597aaf50dfe9f76afb0cc0ae8e05e7c9dcee2b5ba7edb230a3eea0ef2a4059

Observation e544e382-4f78-4d9d-8aa6-775e20ce856e · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.547203Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:f3f423d01a0d8f6b0fcd41431142b7d60fedad8a8360636892a08a852ebe58c6

Observation 3f43e25e-de50-4f29-87ae-5ff5f2984cdd · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.544654Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:a5e66c41fa903382f8426c107535f3fc090fa0a0caca0614d1a747c6b7f5783f

Observation 4d042d82-a278-405e-89d0-d26db612cb66 · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper poses no such risks

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.541764Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:f674aaf0ee1a20da6bb32543c124766ad382980ae8ed745b68f94f844b42b725

Observation 8fb0759b-2afc-4d72-9fd3-cc1d34511cd0 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not use existing assets

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.539329Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:767e1743c716b2e12bdd2f88e6107036e4c7e71850c9389702820da85ac18375

Observation 0268d485-bc56-4cf0-a089-57820444a935 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not release new assets

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.536384Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:99a4dfbade6bd9ab240cd9d63ae25e39cd99c2065ad853d0f51263a0edfff944

Observation eebd1141-07c7-44b3-91d6-63d2e9e2ceed · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.533901Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:477ce80f77f40a9dc0c94336b84cb2811342e84e378601ca8c613a097b793282

Observation 93f1235e-7539-4724-8d9e-2eb0839b17ba · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T03:52:02.531282Z

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.

source=pdf_text observed=2026-05-19T03:47:34.119935Z digest=sha256:5d8bb26829ccc00c6093763412f4b5d33e96009f23f0dc83bdb9d609d1e6d9fe

Pith citing papers

Observation b48a6d3c-02ff-48c8-9642-e4a7ccab2fca · inbound

Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference cites this paper.

Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T10:45:22.019824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:45:22.019824Z digest=sha256:7314f085b332d4d0d12aec0df8cf8719380e39331957dfaaf7c8d130c275b811