From the 1 of 6 linked papers with an AI index.
6 papers
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
The paper presents PLUREL, a lightweight framework for generating synthetic multi-table relational databases, enabling the study of scaling laws in relational foundation models and…
RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
Justin Gu, Rishabh Ranjan, Charilaos Kanatsoulis +8
Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interco…
KumoRFM-2: Scaling Foundation Models for Relational Learning
Valter Hudovernik, Federico López, Vid Kocijan +4
We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fine-tuning and is applicable to…
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Rishabh Ranjan, Valter Hudovernik, Mark Znidar +7
Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and task…
RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models
Valter Hudovernik, Minkai Xu, Juntong Shi +4
Real-world databases are predominantly relational, comprising multiple interlinked tables that contain complex structural and statistical dependencies. Learning generative models o…
Benchmarking the Fidelity and Utility of Synthetic Relational Data
Valter Hudovernik, Martin JurkoviÄ, Erik Å trumbelj
Synthesizing relational data has started to receive more attention from researchers, practitioners, and industry. The task is more difficult than synthesizing a single table due to…