Tabular Transformers Meet Relational Databases
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384310" target="_blank" >RIV/68407700:21230/25:00384310 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3749991" target="_blank" >https://doi.org/10.1145/3749991</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3749991" target="_blank" >10.1145/3749991</a>
Alternative languages
Result language
angličtina
Original language name
Tabular Transformers Meet Relational Databases
Original language description
Transformer models have continuously expanded into all machine learning domains convertible to the underlying sequence-to-sequence representation, including tabular data. However, while ubiquitous, this representation restricts their extension to the more general case of relational databases. In this paper, we introduce a modular neural message-passing scheme that closely adheres to the formal relational model, enabling direct end-to-end learning of tabular transformers from database storage systems. We address the associated challenges of appropriate learning data representation and loading, which are critical in the database setting, and compare our approach against a number of representative models from various related fields across a significantly wide range of datasets. Our results then demonstrate superior performance of this newly proposed class of neural architectures.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA24-11664S" target="_blank" >GA24-11664S: Relational Reinforcement Learning for Science Acceleration</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ACM Transactions on Intelligent Systems and Technology
ISSN
2157-6904
e-ISSN
2157-6912
Volume of the periodical
16
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
24
Pages from-to
555-578
UT code for WoS article
001606000100001
EID of the result in the Scopus database
2-s2.0-105019641424