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Works23 from public data
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- Relational Graph Transformer36
The Relational Graph Transformer (RelGT), the first graph transformer architecture designed specifically for relational tables, is introduced, establishing Graph Transformers as a powerful architecture for Relational Deep Learning.
- Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures28
A comprehensive review of RDL is provided by first introducing the representation of relational databases as relational entity graphs, and then reviewing public benchmark datasets that have been used to develop and evaluate recent GNN-based RDL models.
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- Graph Transformers for Large Graphs17
The proposed GT framework, named LargeGT, overcomes previous computational bottlenecks and is validated on three large-scale node classification benchmarks and is validated on three large-scale node classification benchmarks.
- PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models16
PLUREL is introduced, a framework to synthesize multi-tabular relational databases from scratch and observes for the first time that RFM pretraining loss exhibits power-law scaling with the number of synthetic databases and total pretraining tokens.
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- Representation Learning of Structured Data for Medical Foundation Models9
The UniStruct architecture is introduced to design a multimodal medical foundation model of unstructured text and structured data, which addresses the challenges LLMs face in processing medical codes due to the shortcomings of current tokenization methods.
- Beyond Word2Vec: Embedding Words and Phrases in Same Vector Space8
This work proposes a novel methodology using Siamese deep neural networks to embed multi-word units and tunes the current state-of-the-art word embeddings keeping both in the same vector space.
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- Automated Clinical Coding for Outpatient Departments4
This paper is the first to investigate how well state-of-the-art deep learning-based clinical coding approaches work in the outpatient setting at hospital scale and adapt four state-of-the-art clinical coding approaches to this setting and evaluate their potential to assist coders.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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