2026-08-17T03:58:06Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25253 2026-02-09T12:32:35Z ddc:004 open_access

Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182) Binnig, Carsten Eisenschlos, Julian Martin Hulsebos, Madelon Hutter, Frank applications of table representation learning benchmarks and datasets for table representation learning pre-trained (language) models for tables and databases representation and generative learning for data management and analysis The growing volume and importance of structured data have sparked increasing interest in Table Representation Learning (TRL), an emerging field that leverages neural models to learn abstract, general-purpose representations for tabular data to support a wide range of downstream tasks such as tabular prediction, table question answering, tabular data cleaning, and many more. This seminar gathered the different communities (ML, NLP, IR, DB) who work on this topic to discuss the challenges & long-term vision of this field. From the organizers: Carsten Binnig, Julian Eisenschlos, Madelon Hulsebos, Frank Hutter. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Carsten Binnig and Julian Martin Eisenschlos and Madelon Hulsebos and Frank Hutter 2025 Is Part Of Dagstuhl Reports, Volume 15, Issue 4 (2025) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.15.4.126 urn:nbn:de:0030-drops-252531 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.126 eng https://creativecommons.org/licenses/by/4.0/legalcode