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Paper Citation Record · LEDGER

Relational inductive biases on attention mechanisms

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.04117.

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

pith.paper-citation-record.v1
2507.04117 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:00:04.653991Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

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Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation eae7923e-55f3-42e0-a742-a81cc9f4a3dc · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Relational inductive biases on attention mechanisms TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 1

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Observation 8bc83c28-8a6c-4448-b8f0-2516281c4f76 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Relational inductive biases on attention mechanisms Neural Machine Translation by Jointly Learning to Align and Translate

Reference 2

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Observation 640c2018-b341-4a34-be10-ce2421821928 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Relational inductive biases on attention mechanisms Relational inductive biases, deep learning, and graph networks

Reference 3

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This paper cites Unlimiformer: Long-Range Transformers with Unlimited Length Input.

Relational inductive biases on attention mechanisms Unlimiformer: Long-Range Transformers with Unlimited Length Input

Reference 4

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Observation 3f23013c-df72-40f0-9afa-7093715849be · outbound

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Relational inductive biases on attention mechanisms Unresolved cited work

Reference 5

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This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Relational inductive biases on attention mechanisms Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 6

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This paper cites IEEE Signal Processing Magazine 34(4), 18–42 (2017).

Relational inductive biases on attention mechanisms IEEE Signal Processing Magazine 34(4), 18–42 (2017)

Reference 7

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This paper cites The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science7(42), 40–47 (1854).

Relational inductive biases on attention mechanisms The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science7(42), 40–47 (1854)

Reference 8

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Observation 7561f207-78c7-44d7-ad99-dfd85dbe01bd · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Relational inductive biases on attention mechanisms Generating Long Sequences with Sparse Transformers

Reference 9

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This paper cites In: Linzen, T., Chrupała, G., Belinkov, Y ., Hupkes, D.

Relational inductive biases on attention mechanisms In: Linzen, T., Chrupała, G., Belinkov, Y ., Hupkes, D

Reference 10

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This paper cites In: Korhonen, A., Traum, D., M`arquez, L.

Relational inductive biases on attention mechanisms In: Korhonen, A., Traum, D., M`arquez, L

Reference 11

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This paper cites In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers).

Relational inductive biases on attention mechanisms In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)

Reference 12

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Relational inductive biases on attention mechanisms An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Relational inductive biases on attention mechanisms Position: Categorical Deep Learning is an Algebraic Theory of All Architectures

Reference 14

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Relational inductive biases on attention mechanisms Efficiently Modeling Long Sequences with Structured State Spaces

Reference 15

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Observation 43efc32e-bc75-4dfe-b6f0-79d4e1b552d5 · outbound

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Relational inductive biases on attention mechanisms In: The Thirty-Fifth AAAI Conference on Artificial Intelligence

Reference 16

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Relational inductive biases on attention mechanisms Advances in applied analysis pp

Reference 17

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This paper cites IEEE Transactions on Visualization and Computer Graphics 29(6), 2888–2900 (2023).

Relational inductive biases on attention mechanisms IEEE Transactions on Visualization and Computer Graphics 29(6), 2888–2900 (2023)

Reference 18

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Relational inductive biases on attention mechanisms IEEE Transactions on Neural Networks and Learning Systems (2023)

Reference 19

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Relational inductive biases on attention mechanisms Transformers in 3D Point Clouds: A Survey

Reference 20

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Relational inductive biases on attention mechanisms Readings in Machine Learning (1980)

Reference 21

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Relational inductive biases on attention mechanisms Unresolved cited work

Reference 22

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Relational inductive biases on attention mechanisms Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks

Reference 23

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Relational inductive biases on attention mechanisms Advances in neural information processing systems 32 (2019)

Reference 24

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Relational inductive biases on attention mechanisms Unresolved cited work

Reference 25

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Relational inductive biases on attention mechanisms Journal of machine learning research 21(140), 1–67 (2020)

Reference 26

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Relational inductive biases on attention mechanisms In: Proceedings of the 36th International Conference on Neural Information Processing Systems

Reference 27

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Relational inductive biases on attention mechanisms In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Reference 28

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Relational inductive biases on attention mechanisms Attention Is All You Need

Reference 29

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Relational inductive biases on attention mechanisms Graph Attention Networks

Reference 30

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Pith citing papers

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