Converting Transformers into DGNNs Form | Paper Citation Record

Observation eefec7e6-19e3-4882-a655-7724bf2cfe91 · outbound

This paper cites Attention Is All You Need.

Converting Transformers into DGNNs Form Attention Is All You Need

Reference 1

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Observation 282dc5b5-cbce-4f47-bd10-4fd21fc0c969 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Converting Transformers into DGNNs Form BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.070083Z

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Observation 6fa06e2d-4a5c-44af-9415-ff88ef408932 · outbound

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

Converting Transformers into DGNNs Form An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.074511Z

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Observation dee04d84-42d2-41a2-9160-786b16c6a3ea · outbound

This paper cites Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks.

Converting Transformers into DGNNs Form Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Reference 4

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Observation 0a9dd51a-296a-485f-a6d2-323adf79f9d2 · outbound

This paper cites Transformer Dissection: An Unified Understanding for Transformer ' s Attention via the Lens of Kernel.

Converting Transformers into DGNNs Form Transformer Dissection: An Unified Understanding for Transformer ' s Attention via the Lens of Kernel

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.084447Z

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Observation b3205813-7033-486c-8d7f-3bb23661276d · outbound

This paper cites Rethinking Attention with Performers.

Converting Transformers into DGNNs Form Rethinking Attention with Performers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.088826Z

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Observation 95c2a30a-2a14-4fc5-9684-e483e21a6d9e · outbound

This paper cites cosFormer: Rethinking Softmax In Attention.

Converting Transformers into DGNNs Form cosFormer: Rethinking Softmax In Attention

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.794024Z

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Observation bf56dc39-03f7-4c01-977c-8d28f01696a7 · outbound

This paper cites Attention is not all you need: pure attention loses rank doubly exponentially with depth.

Converting Transformers into DGNNs Form Attention is not all you need: pure attention loses rank doubly exponentially with depth

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.779306Z

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Observation 7367f092-c063-4040-87cf-470db5a51f27 · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:29.764387Z

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Observation e9b4dec0-220d-4181-a461-82b1607988cb · outbound

This paper cites Softmax is not Enough (for Sharp Size Generalisation).

Converting Transformers into DGNNs Form Softmax is not Enough (for Sharp Size Generalisation)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.106310Z

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Observation fbab42ef-5a1d-4739-b080-3c5db0f424a6 · outbound

This paper cites Synthesizer: Rethinking Self-Attention for Transformer Models.

Converting Transformers into DGNNs Form Synthesizer: Rethinking Self-Attention for Transformer Models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.750023Z

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Observation 8119e2c4-9f07-4e9b-ac1c-c40aaa6525a9 · outbound

This paper cites FNet: Mixing Tokens with Fourier Transforms.

Converting Transformers into DGNNs Form FNet: Mixing Tokens with Fourier Transforms

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.735127Z

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Observation 39cfe3df-227d-43b5-ae26-bb2b6300003f · outbound

This paper cites Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention.

Converting Transformers into DGNNs Form Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.720361Z

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Observation 896001d3-9764-461d-a129-54897687429a · outbound

This paper cites Big Bird: Transformers for Longer Sequences.

Converting Transformers into DGNNs Form Big Bird: Transformers for Longer Sequences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.705264Z

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Observation cd77821a-8c3b-435c-89c7-42b7ce540d9d · outbound

This paper cites Silver and H.

Converting Transformers into DGNNs Form Silver and H

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.689835Z

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Observation f54c3283-3faa-4559-a26a-770e75f50d49 · outbound

This paper cites Calculating the density of states and optical-absorption spectra of large quantum systems by the plane-wave moments method.

Converting Transformers into DGNNs Form Calculating the density of states and optical-absorption spectra of large quantum systems by the plane-wave moments method

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.675013Z

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Observation 0ff3f22d-0656-4217-b888-69e7cb18e5e7 · outbound

This paper cites Dielectric Constants of Silicon Quantum Dots.

Converting Transformers into DGNNs Form Dielectric Constants of Silicon Quantum Dots

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.660060Z

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Observation 0c5048e5-1c26-40d2-a02e-a507c42a71fd · outbound

This paper cites Kouri, and David K.

Converting Transformers into DGNNs Form Kouri, and David K

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.644821Z

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Observation a3febc57-0d91-4ce5-a387-2a9ac04f0d7e · outbound

This paper cites The kernel polynomial method.

Converting Transformers into DGNNs Form The kernel polynomial method

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.629642Z

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Observation 35ce54d5-cbf9-4f85-ab9f-1d32eef91889 · outbound

This paper cites Chebyshev Expansion Techniques , pages 545--577.

Converting Transformers into DGNNs Form Chebyshev Expansion Techniques , pages 545--577

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.614870Z

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Observation 5b6ce05e-e67d-4c85-92a6-dfe164b9f44c · outbound

This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

Converting Transformers into DGNNs Form Long Range Arena: A Benchmark for Efficient Transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.600866Z

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Observation 7429ecdb-8383-4a3f-ae62-5d5f9502f2e1 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Converting Transformers into DGNNs Form Generating Long Sequences with Sparse Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.261570Z

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Observation a9bf7277-8fb7-4322-8fa7-89fb196bb46a · outbound

This paper cites Reformer: The Efficient Transformer.

Converting Transformers into DGNNs Form Reformer: The Efficient Transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.587336Z

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Observation df928dac-3c22-499f-a2e1-272dea811a8b · outbound

This paper cites Scatterbrain: Unifying sparse and low-rank attention.

Converting Transformers into DGNNs Form Scatterbrain: Unifying sparse and low-rank attention

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.573230Z

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Observation 7ea2925f-6730-47db-a3e8-a88fc080143c · outbound

This paper cites MetaFormer Baselines for Vision.

Converting Transformers into DGNNs Form MetaFormer Baselines for Vision

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.275588Z

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Observation 6e452274-cc7e-4226-9461-ac56b4cb57d3 · outbound

This paper cites MetaFormer Is Actually What You Need for Vision.

Converting Transformers into DGNNs Form MetaFormer Is Actually What You Need for Vision

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.558727Z

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Observation 22170dbf-a1f8-4491-8210-c616157d12c2 · outbound

This paper cites Are Sixteen Heads Really Better than One? In H.

Converting Transformers into DGNNs Form Are Sixteen Heads Really Better than One? In H

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.543331Z

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Observation d3ac884b-3e6a-4844-ae1e-337166f75aa3 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Converting Transformers into DGNNs Form Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.528662Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3deac9a4-7d53-4ff4-b3b5-60c9f66dccfd · outbound

This paper cites Multi-Head Attention: Collaborate Instead of Concatenate.

Converting Transformers into DGNNs Form Multi-Head Attention: Collaborate Instead of Concatenate

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.294311Z

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Observation 4069160d-552b-41fb-92e1-f6bb4e2897fb · outbound

This paper cites Low-Rank Bottleneck in Multi-head Attention Models.

Converting Transformers into DGNNs Form Low-Rank Bottleneck in Multi-head Attention Models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.513360Z

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Observation c1a87787-5bde-43cf-8e11-651594ce9b04 · outbound

This paper cites Graph filters for signal processing and machine learning on graphs.

Converting Transformers into DGNNs Form Graph filters for signal processing and machine learning on graphs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.497890Z

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Observation 7f0d77b2-31ee-4641-a11e-2b2ec1dd15bf · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:29.482625Z

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Observation 19e922f7-597f-424a-aed0-b36a8dbf78c9 · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:29.467914Z

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Observation 61fc204f-6349-428e-955e-4e4db36815c7 · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:29.452001Z

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Observation eee80af5-a5e5-4ecb-bc0f-eda0dda2aa87 · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:29.436770Z

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Observation 4b8c2f18-2953-4449-8dc7-21f9af801a05 · outbound

This paper cites Laplacians and the cheeger inequality for directed graphs.

Converting Transformers into DGNNs Form Laplacians and the cheeger inequality for directed graphs

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.421581Z

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Observation b2299af2-587f-4a01-9a73-8a4904310e4e · outbound

This paper cites Ala \' i z, and Johan A.

Converting Transformers into DGNNs Form Ala \' i z, and Johan A

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.406653Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4923656d-a92c-41b7-a504-cfbe9867244e · outbound

This paper cites Ala \' i z, \' A ngela Fern \' a ndez, and Johan A.K.

Converting Transformers into DGNNs Form Ala \' i z, \' A ngela Fern \' a ndez, and Johan A.K

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.392663Z

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Observation d9d3ece8-24d9-4494-ae26-24d1de33989a · outbound

This paper cites Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform.

Converting Transformers into DGNNs Form Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.378088Z

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Observation 668a7bc3-642e-4bf6-b8bb-471aadec6af7 · outbound

This paper cites A sparse Johnson: Lindenstrauss transform.

Converting Transformers into DGNNs Form A sparse Johnson: Lindenstrauss transform

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.363560Z

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Observation 7849e95d-6135-433c-8306-d2c7fde6880a · outbound

This paper cites Fastfood — Approximating Kernel Expansions in Loglinear Time.

Converting Transformers into DGNNs Form Fastfood — Approximating Kernel Expansions in Loglinear Time

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.349299Z

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Observation 90426f4f-348b-4ccb-8848-f64305d8a465 · outbound

This paper cites Orthogonal Random Features.

Converting Transformers into DGNNs Form Orthogonal Random Features

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.334011Z

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Observation 46e65fea-93f9-4399-a686-72c7f941437a · outbound

This paper cites Deep Fried Convnets.

Converting Transformers into DGNNs Form Deep Fried Convnets

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.319032Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ff3c547-a1b5-408a-a4d2-c2cac212e2c1 · outbound

This paper cites ACDC: A Structured Efficient Linear Layer.

Converting Transformers into DGNNs Form ACDC: A Structured Efficient Linear Layer

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.303960Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6bd19458-5d99-451f-8edc-fa5e5f43d324 · outbound

This paper cites Hammond, Pierre Vandergheynst, and R \' e mi Gribonval.

Converting Transformers into DGNNs Form Hammond, Pierre Vandergheynst, and R \' e mi Gribonval

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.289238Z

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Observation 2046b798-8567-47a9-8d66-c0898aa10521 · outbound

This paper cites Implicit Neural Representations with Periodic Activation Functions.

Converting Transformers into DGNNs Form Implicit Neural Representations with Periodic Activation Functions

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.274102Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5e3694ba-47e3-402a-b894-249a49a06499 · outbound

This paper cites Computation of Plain Unitary Rotations Transforming a General Matrix to Triangular Form.

Converting Transformers into DGNNs Form Computation of Plain Unitary Rotations Transforming a General Matrix to Triangular Form

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.259029Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9281dbaa-e5c4-4078-ab39-f6903c99845c · outbound

This paper cites Learning Latent Permutations with Gumbel-Sinkhorn Networks.

Converting Transformers into DGNNs Form Learning Latent Permutations with Gumbel-Sinkhorn Networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.243510Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c964c62-36b9-4534-bb2f-b6ea572e783e · outbound

This paper cites Monarch: Expressive Structured Matrices for Efficient and Accurate Training.

Converting Transformers into DGNNs Form Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.227974Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e598b062-8c0c-4f77-b834-7bcd8b49e068 · outbound

This paper cites Sparse factorization of square matrices with application to neural attention modeling.

Converting Transformers into DGNNs Form Sparse factorization of square matrices with application to neural attention modeling

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.212335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16d2c194-5afb-4db6-9240-076b2cb00acd · outbound

This paper cites Fast Training of Convolutional Networks through FFTs.

Converting Transformers into DGNNs Form Fast Training of Convolutional Networks through FFTs

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.196949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b9df576-20a7-4609-942e-72f7a915b58e · outbound

This paper cites Spectral Graph Theory.

Converting Transformers into DGNNs Form Spectral Graph Theory

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.181839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4228b797-eaad-4c45-8f20-c5fbf2d071ea · outbound

This paper cites Trefethen.

Converting Transformers into DGNNs Form Trefethen

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.167985Z

Source-reported events for the cited work

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Observation bf2d5281-72c1-4533-83c5-a3e6d231e5c6 · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:29.153647Z

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Observation df6ca1d9-ee49-4a94-bc5d-7b95f4be0078 · outbound

This paper cites Wong, and Lidia S.

Converting Transformers into DGNNs Form Wong, and Lidia S

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.139380Z

Source-reported events for the cited work

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Observation d3cbc5d4-0f10-4c93-93f8-0efac0689e1f · outbound

This paper cites Nguyen and Julian Salazar.

Converting Transformers into DGNNs Form Nguyen and Julian Salazar

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.124177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 039f2e1c-119a-40bf-905d-4f038e8746f2 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Converting Transformers into DGNNs Form PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.109125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63bf3e08-1cce-4f55-82eb-ff26c2ec085d · outbound

This paper cites On the Relation between Position Information and Sentence Length in Neural Machine Translation.

Converting Transformers into DGNNs Form On the Relation between Position Information and Sentence Length in Neural Machine Translation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.093122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 85a2f459-99ed-45ac-8f57-102e79f41c3e · outbound

This paper cites Decoupled Weight Decay Regularization.

Converting Transformers into DGNNs Form Decoupled Weight Decay Regularization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.077368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ed3bbd3-199b-452a-9633-2efb257a144e · outbound

This paper cites Longformer: The Long-Document Transformer.

Converting Transformers into DGNNs Form Longformer: The Long-Document Transformer

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.436546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7411095-5694-4ebf-aee1-e264e7559771 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Converting Transformers into DGNNs Form Linformer: Self-Attention with Linear Complexity

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T18:31:28.441503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5671b8e0-36f2-43a5-b326-f6872bff4e08 · outbound

This paper cites Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention.

Converting Transformers into DGNNs Form Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.061056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 07443323-63cb-4534-ab34-5726cf475147 · outbound

This paper cites Sparse Sinkhorn Attention.

Converting Transformers into DGNNs Form Sparse Sinkhorn Attention

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.045393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f22ec0d7-cfce-40b5-acd9-50dc04f313cf · outbound

This paper cites o mformer: A Nystr \.

Converting Transformers into DGNNs Form o mformer: A Nystr \

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.030896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57570793-4ad0-4f4b-bd50-7cc783c16f29 · outbound

This paper cites Luna: Linear Unified Nested Attention.

Converting Transformers into DGNNs Form Luna: Linear Unified Nested Attention

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.015637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4bcbcae3-4589-4abf-8cba-836241ee7945 · outbound

This paper cites ListOps: A Diagnostic Dataset for Latent Tree Learning.

Converting Transformers into DGNNs Form ListOps: A Diagnostic Dataset for Latent Tree Learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:29.000767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7bc4bd1-cfe9-499e-8936-8ce91ea80910 · outbound

This paper cites Maas, Raymond E.

Converting Transformers into DGNNs Form Maas, Raymond E

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.985890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 855b8a2b-1e25-4978-9f47-6c6465d3d298 · outbound

This paper cites Radev, Pradeep Muthukrishnan, and Vahed Qazvinian.

Converting Transformers into DGNNs Form Radev, Pradeep Muthukrishnan, and Vahed Qazvinian

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.970122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 94658e83-d921-4a82-a1ce-1f588aad78a0 · outbound

This paper cites Learning Multiple Layers of Features from Tiny Images.

Converting Transformers into DGNNs Form Learning Multiple Layers of Features from Tiny Images

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.954696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 28071fa1-95af-4200-a7ec-4f2b4c08042d · outbound

This paper cites Learning long-range spatial dependencies with horizontal gated recurrent units.

Converting Transformers into DGNNs Form Learning long-range spatial dependencies with horizontal gated recurrent units

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.940113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3434fc92-de96-4150-9086-ec0cd2c8ae9b · outbound

This paper cites Disentangling neural mechanisms for perceptual grouping.

Converting Transformers into DGNNs Form Disentangling neural mechanisms for perceptual grouping

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.925605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b50f3145-fd04-461b-a2c0-1b845c624cd6 · outbound

This paper cites Parallel and serial grouping of image elements in visual perception.

Converting Transformers into DGNNs Form Parallel and serial grouping of image elements in visual perception

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.911525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b7126307-99da-4835-94e7-d52424aee4f4 · outbound

This paper cites Long length document classification by local convolutional feature aggregation.

Converting Transformers into DGNNs Form Long length document classification by local convolutional feature aggregation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.896078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca99e908-f73f-49fe-9a7b-5adbfcc127ba · outbound

This paper cites an unresolved cited work.

Converting Transformers into DGNNs Form Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:31:28.881026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b075940-2212-445a-9dc1-1fb8fe6adefc · outbound

This paper cites Transformer Language Models without Positional Encodings Still Learn Positional Information.

Converting Transformers into DGNNs Form Transformer Language Models without Positional Encodings Still Learn Positional Information

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.866310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d474e731-e756-4dc4-ba5d-2e5ed2ff142c · outbound

This paper cites Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings.

Converting Transformers into DGNNs Form Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.850876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27f9cfbf-bf34-4372-ac56-909ca13c60bc · outbound

This paper cites The Impact of Positional Encoding on Length Generalization in Transformers.

Converting Transformers into DGNNs Form The Impact of Positional Encoding on Length Generalization in Transformers

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.834894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 97d23265-76e9-4ec0-bcdf-ba7e647fec8c · outbound

This paper cites Choose a Transformer: Fourier or Galerkin.

Converting Transformers into DGNNs Form Choose a Transformer: Fourier or Galerkin

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.818798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 84f4cbf4-a0a5-4fa4-a388-dc24d76f008d · outbound

This paper cites Efficient Attention: Attention With Linear Complexities.

Converting Transformers into DGNNs Form Efficient Attention: Attention With Linear Complexities

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.803860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7ccab9fb-210f-423c-98c6-58c1747bfee2 · outbound

This paper cites Sparse Attention with Linear Units.

Converting Transformers into DGNNs Form Sparse Attention with Linear Units

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.788189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e0b0d1d-fbe6-4d32-a016-bc6162163b33 · outbound

This paper cites SimA: Simple Softmax-Free Attention for Vision Transformers.

Converting Transformers into DGNNs Form SimA: Simple Softmax-Free Attention for Vision Transformers

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.771280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7486a580-d18f-4fd8-af0f-a489ec8c46ee · outbound

This paper cites Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr \" o m Method.

Converting Transformers into DGNNs Form Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr \" o m Method

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.753658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee629e88-99b3-4c12-9880-42b5e4ea83ab · outbound

This paper cites Scalable Parallel Programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for? Queue, 6 0 (2): 0 40--53, 03 2008.

Converting Transformers into DGNNs Form Scalable Parallel Programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for? Queue, 6 0 (2): 0 40--53, 03 2008

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.737615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fdcf6b47-0ede-47b2-ab91-8b7539b65590 · outbound

This paper cites Untersuchungen \"u ber Fouriersche Reihen.

Converting Transformers into DGNNs Form Untersuchungen \"u ber Fouriersche Reihen

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.720870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b0ec432-45aa-4259-93ee-4d8274f1ed74 · outbound

This paper cites Discourse on Fourier series.

Converting Transformers into DGNNs Form Discourse on Fourier series

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.706021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6cc4ed66-9b77-4c36-9de2-90902e967fce · outbound

This paper cites Veki \' c and S.

Converting Transformers into DGNNs Form Veki \' c and S

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:31:28.690768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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