Paper Citation Record · LEDGER

Transformers Meet Relational Databases

As of 11 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 3 inbound Pith citation observations for arXiv:2412.05218.

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

pith.paper-citation-record.v1
2412.05218 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:54:08.990395Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:45:28.119622Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:36:27.494966Z

Reference resolution

69 of 69 outbound references displayed

verified exact0
verified fuzzy42
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f296a9e0-99d4-4712-839c-411f2f635698 · outbound

This paper cites James, D.

Transformers Meet Relational Databases James, D

Reference 1

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Source-reported events for the cited work

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Observation ba6804f0-10a7-4fd4-94ec-4678370c59b1 · outbound

This paper cites Halpin and T.

Transformers Meet Relational Databases Halpin and T

Reference 2

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Observation 58c2d705-6371-4046-9961-adbfa497154a · outbound

This paper cites Greedy function approximation: a gradient boosting machine,.

Transformers Meet Relational Databases Greedy function approximation: a gradient boosting machine,

Reference 3

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Source-reported events for the cited work

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Observation 86853945-c1a7-403c-9a43-6177f9c4ebb3 · outbound

This paper cites Tabular data: Deep learning is not all you need,.

Transformers Meet Relational Databases Tabular data: Deep learning is not all you need,

Reference 4

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Source-reported events for the cited work

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Observation 2384fff0-4483-49a0-9583-051c34feb022 · outbound

This paper cites Attention is all you need,.

Transformers Meet Relational Databases Attention is all you need,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.907071Z

Source-reported events for the cited work

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Observation 0fed141a-be35-4937-96ef-2e9c4fe3fd2c · outbound

This paper cites Well-tuned simple nets excel on tabular datasets,.

Transformers Meet Relational Databases Well-tuned simple nets excel on tabular datasets,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.895095Z

Source-reported events for the cited work

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Observation fabc7e6f-f50a-41c7-b608-6629c66130fa · outbound

This paper cites Getoor and B.

Transformers Meet Relational Databases Getoor and B

Reference 7

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Source-reported events for the cited work

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Observation b0980a34-fa28-40e3-8d1f-d30608baf601 · outbound

This paper cites Transformers for tabular data representation: A survey of models and applications,.

Transformers Meet Relational Databases Transformers for tabular data representation: A survey of models and applications,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.883397Z

Source-reported events for the cited work

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Observation 5260d0b4-7baa-4dc0-8bef-02edcbec2be3 · outbound

This paper cites Tabnet: Attentive interpretable tabular learning,.

Transformers Meet Relational Databases Tabnet: Attentive interpretable tabular learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.860743Z

Source-reported events for the cited work

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This paper cites Saint: Improved neural networks for tabular data via row attention and contrastive pre-training,.

Transformers Meet Relational Databases Saint: Improved neural networks for tabular data via row attention and contrastive pre-training,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d74ac18d-992b-45d6-839b-727597a1cfe6 · outbound

This paper cites Trompt: Towards a better deep neural network for tabular data,.

Transformers Meet Relational Databases Trompt: Towards a better deep neural network for tabular data,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.836987Z

Source-reported events for the cited work

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Observation 268b67e3-4745-4530-a3f6-fd438597ddad · outbound

This paper cites Turning 30: New Ideas in Inductive Logic Programming.

Transformers Meet Relational Databases Turning 30: New Ideas in Inductive Logic Programming

Reference 13

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Source-reported events for the cited work

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Observation 76e88745-16b1-443d-b99f-bef272e6a5f2 · outbound

This paper cites De Raedt, Logical and relational learning.

Transformers Meet Relational Databases De Raedt, Logical and relational learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.823614Z

Source-reported events for the cited work

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Observation 8fa25ab0-adc4-48cd-800d-81fb1f5e8450 · outbound

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Transformers Meet Relational Databases Unresolved cited work

Reference 15

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Source-reported events for the cited work

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Observation 8df43722-e027-467c-b411-4bd2556b09e1 · outbound

This paper cites Inductive logic programming: Theory and methods,.

Transformers Meet Relational Databases Inductive logic programming: Theory and methods,

Reference 16

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Observation d056119e-461a-4144-af53-920a216169ea · outbound

This paper cites Lifted graphical models: a survey,.

Transformers Meet Relational Databases Lifted graphical models: a survey,

Reference 17

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Observation 7336eb50-6d1c-4607-a054-2f69675fad4d · outbound

This paper cites Propositionalization approaches to relational data mining,.

Transformers Meet Relational Databases Propositionalization approaches to relational data mining,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.774641Z

Source-reported events for the cited work

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Observation eb25d4a8-4606-4e93-9144-c07f1068a484 · outbound

This paper cites Featuretools.

Transformers Meet Relational Databases Featuretools

Reference 19

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Source-reported events for the cited work

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Observation 426f073a-bb7c-4cbb-a0b0-a1f58eee7a3a · outbound

This paper cites Available: https: //getml.com.

Transformers Meet Relational Databases Available: https: //getml.com

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.751071Z

Source-reported events for the cited work

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Observation 0f368687-6381-4ac7-873d-202a4fc6c6e1 · outbound

This paper cites Hammer and P.

Transformers Meet Relational Databases Hammer and P

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.739257Z

Source-reported events for the cited work

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Observation 9c1c8c05-f024-4a7d-a0a0-9f2aab0138df · outbound

This paper cites Learning knowledge base inference with neural theorem provers,.

Transformers Meet Relational Databases Learning knowledge base inference with neural theorem provers,

Reference 22

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Source-reported events for the cited work

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Observation c9628ee9-1f4c-4597-8f93-0709c697f08f · outbound

This paper cites Learning and reasoning with logic tensor networks,.

Transformers Meet Relational Databases Learning and reasoning with logic tensor networks,

Reference 23

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Source-reported events for the cited work

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Observation b25e2a7e-16b0-4455-828b-f501d0680882 · outbound

This paper cites Lifted relational neural networks: Efficient learning of latent relational structures,.

Transformers Meet Relational Databases Lifted relational neural networks: Efficient learning of latent relational structures,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.698784Z

Source-reported events for the cited work

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Observation 1b18c4a7-4de0-4826-ab97-115be3203443 · outbound

This paper cites Fast relational learning using bottom clause propositionalization with artificial neural networks,.

Transformers Meet Relational Databases Fast relational learning using bottom clause propositionalization with artificial neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.685733Z

Source-reported events for the cited work

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Observation ec731528-97af-4e58-aeb9-eada33a27a3c · outbound

This paper cites A comprehensive survey on graph neural networks,.

Transformers Meet Relational Databases A comprehensive survey on graph neural networks,

Reference 26

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Source-reported events for the cited work

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Observation 90127949-b7b7-413a-a798-b8fc0b51f94e · outbound

This paper cites The graph neural network model,.

Transformers Meet Relational Databases The graph neural network model,

Reference 27

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Transformers Meet Relational Databases Hypergraph neural net- works,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.653660Z

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Observation 37ae7826-7a94-4a22-a1d3-9d6b70d64299 · outbound

This paper cites Modeling relational data with graph convolutional networks,.

Transformers Meet Relational Databases Modeling relational data with graph convolutional networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.641558Z

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Observation 1cc77adf-dba1-436c-954a-f54f8e05ffe3 · outbound

This paper cites Knowledge graph embed- ding: A survey of approaches and applications,.

Transformers Meet Relational Databases Knowledge graph embed- ding: A survey of approaches and applications,

Reference 30

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Observation ea0c28fa-fb91-459a-9161-f8bdc5772a1b · outbound

This paper cites Supervised Learning on Relational Databases with Graph Neural Networks.

Transformers Meet Relational Databases Supervised Learning on Relational Databases with Graph Neural Networks

Reference 31

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Observation 3fb95b18-49b6-425c-9bcc-a67b7fc871be · outbound

This paper cites Flaky performances when pre-training on relational databases with a plan for future characteriza- tion efforts,.

Transformers Meet Relational Databases Flaky performances when pre-training on relational databases with a plan for future characteriza- tion efforts,

Reference 32

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Observation 1767145e-513e-48f5-88c7-c17de6a4aea1 · outbound

This paper cites Atj-net: Auto- table-join network for automatic learning on relational databases,.

Transformers Meet Relational Databases Atj-net: Auto- table-join network for automatic learning on relational databases,

Reference 33

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Observation 5b2fdf16-29cc-4794-b2a3-7416f64d2403 · outbound

This paper cites Turl: Table understanding through representation learning,.

Transformers Meet Relational Databases Turl: Table understanding through representation learning,

Reference 34

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Observation 8501f8a5-ea6c-4f48-96ef-09673fa8bcd2 · outbound

This paper cites Teaching old db neu(ral) tricks: Learning embeddings on multi-tabular databases,.

Transformers Meet Relational Databases Teaching old db neu(ral) tricks: Learning embeddings on multi-tabular databases,

Reference 35

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Observation 5f045967-6bb0-4fea-b947-c78ae0f71500 · outbound

This paper cites Towards Foundation Models for Relational Databases [Vision Paper].

Transformers Meet Relational Databases Towards Foundation Models for Relational Databases [Vision Paper]

Reference 36

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Observation 539596d8-739f-47ae-aa89-83a9bc02593d · outbound

This paper cites SPARE: A Single-Pass Neural Model for Relational Databases.

Transformers Meet Relational Databases SPARE: A Single-Pass Neural Model for Relational Databases

Reference 37

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Observation 54ab79bd-092a-4cbc-b5a7-1a8497cca98b · outbound

This paper cites Position: Relational deep learning - graph representation learning on relational databases,.

Transformers Meet Relational Databases Position: Relational deep learning - graph representation learning on relational databases,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.579284Z

Source-reported events for the cited work

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Observation a8b96f8c-6563-45c5-9ddd-25e374c1b516 · outbound

This paper cites Beyond graphs: Learning with relational DBs,.

Transformers Meet Relational Databases Beyond graphs: Learning with relational DBs,

Reference 39

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Source-reported events for the cited work

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Observation 3cefa20c-33ce-4044-a5bf-8697643acc43 · outbound

This paper cites GFS: Graph-based Feature Synthesis for Prediction over Relational Databases.

Transformers Meet Relational Databases GFS: Graph-based Feature Synthesis for Prediction over Relational Databases

Reference 40

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This paper cites A deep learning blueprint for relational databases,.

Transformers Meet Relational Databases A deep learning blueprint for relational databases,

Reference 41

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Transformers Meet Relational Databases Unresolved cited work

Reference 42

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This paper cites Goodfellow, Y.

Transformers Meet Relational Databases Goodfellow, Y

Reference 43

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This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Transformers Meet Relational Databases Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 44

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Transformers Meet Relational Databases PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning

Reference 45

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Transformers Meet Relational Databases Inductive representation learning on large graphs,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.519794Z

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Observation ab2362c9-2c20-4774-a107-bdab54944cd1 · outbound

This paper cites Heterogeneous graph trans- former,.

Transformers Meet Relational Databases Heterogeneous graph trans- former,

Reference 47

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Source-reported events for the cited work

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Transformers Meet Relational Databases Heterogeneous graph neural network,

Reference 48

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This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.

Transformers Meet Relational Databases TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 49

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Source-reported events for the cited work

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This paper cites Tracr: Compiled Transformers as a Laboratory for Interpretability.

Transformers Meet Relational Databases Tracr: Compiled Transformers as a Laboratory for Interpretability

Reference 50

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Source-reported events for the cited work

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This paper cites The CTU Prague Relational Learning Repository.

Transformers Meet Relational Databases The CTU Prague Relational Learning Repository

Reference 51

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Source-reported events for the cited work

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This paper cites UCI Machine Learning Repository,.

Transformers Meet Relational Databases UCI Machine Learning Repository,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.499288Z

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Observation d36c4fc5-19ab-4bc8-be33-431407a6bca9 · outbound

This paper cites Explain- ing queries over web tables to non-experts,.

Transformers Meet Relational Databases Explain- ing queries over web tables to non-experts,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.486692Z

Source-reported events for the cited work

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Observation dd9bf47f-2870-486e-a68a-b6af1abf1928 · outbound

This paper cites Mimic-iv (version 1.0),.

Transformers Meet Relational Databases Mimic-iv (version 1.0),

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.473416Z

Source-reported events for the cited work

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Observation 41acd54f-b19e-41f5-9e10-1155733e6d50 · outbound

This paper cites SNAP Datasets: Stanford large network dataset collection,.

Transformers Meet Relational Databases SNAP Datasets: Stanford large network dataset collection,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:08.941324Z

Source-reported events for the cited work

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Observation 0df17b9e-5d65-4816-b929-9ed2b149fd8a · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs,.

Transformers Meet Relational Databases Open graph benchmark: Datasets for machine learning on graphs,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.453763Z

Source-reported events for the cited work

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Observation 4727c38b-ad05-4f8f-8cc4-ed354910b2d9 · outbound

This paper cites Relbench: A benchmark for deep learning on relational databases,.

Transformers Meet Relational Databases Relbench: A benchmark for deep learning on relational databases,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.441745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5c59c956-2d4f-4fd4-9ae8-4529fd7a0132 · outbound

This paper cites Wikidbs: A large-scale corpus of relational databases from wikidata,.

Transformers Meet Relational Databases Wikidbs: A large-scale corpus of relational databases from wikidata,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:09.428983Z

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This paper cites Gradient-based boosting for statistical relational learning: The rela- tional dependency network case,.

Transformers Meet Relational Databases Gradient-based boosting for statistical relational learning: The rela- tional dependency network case,

Reference 59

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This paper cites Xgboost: A scalable tree boosting system,.

Transformers Meet Relational Databases Xgboost: A scalable tree boosting system,

Reference 60

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This paper cites Inductive representation learning on large graphs,.

Transformers Meet Relational Databases Inductive representation learning on large graphs,

Reference 61

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This paper cites Layer normalization,.

Transformers Meet Relational Databases Layer normalization,

Reference 62

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This paper cites Krogel, S.

Transformers Meet Relational Databases Krogel, S

Reference 63

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Observation 855afa8b-e8bc-4b4d-96df-eba811e566ad · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Transformers Meet Relational Databases Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 64

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Observation 7e9d5d26-4675-4c31-8afe-8225287405c7 · outbound

This paper cites Ray: A distributed framework for emerging ai applications,.

Transformers Meet Relational Databases Ray: A distributed framework for emerging ai applications,

Reference 65

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Observation 5636c4d4-e821-41e0-ab9e-d7cb931b14c5 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework,.

Transformers Meet Relational Databases Optuna: A next-generation hyperparameter optimization framework,

Reference 66

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Transformers Meet Relational Databases Accelerat- ing the machine learning lifecycle with mlflow

Reference 67

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This paper cites Adam: A Method for Stochastic Optimization.

Transformers Meet Relational Databases Adam: A Method for Stochastic Optimization

Reference 68

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This paper cites Excelformer: A neural network surpassing gbdts on tabular data,.

Transformers Meet Relational Databases Excelformer: A neural network surpassing gbdts on tabular data,

Reference 69

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Observation ba7e82d3-1cab-4951-9474-dd76d3c3ad8d · outbound

This paper cites Available: https://aclanthology.org/2023.tacl-1.14.

Transformers Meet Relational Databases Available: https://aclanthology.org/2023.tacl-1.14

Reference 2023

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Pith citing papers

Observation 484dccad-e09f-401d-933b-1c7ce1f39df8 · inbound

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data cites this paper.

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data Transformers Meet Relational Databases

Reference 10

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Observation fb0c1f37-e44f-4995-98f1-4765f73b5338 · inbound

From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics cites this paper.

From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics Transformers Meet Relational Databases

Reference 34

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Observation 42dbb9f7-819b-463e-aac2-3f19645a61a8 · inbound

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases cites this paper.

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases Transformers Meet Relational Databases

Reference 29

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