7 papers
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…
Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
Vid Kocijan, Jinu Sunil, Jan Eric Lenssen +5
The purpose of predictive modeling on relational data is to predict future or missing values in a relational database, for example, future purchases of a user, risk of readmission…
Relational Graph Transformer
Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen +5
Relational Deep Learning (RDL) is a promising approach for building state-of-the-art predictive models on multi-table relational data by representing it as a heterogeneous temporal…
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal…
PyG 2.0: Scalable Learning on Real World Graphs
Matthias Fey, Jinu Sunil, Akihiro Nitta +10
PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0…
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
Weihua Hu, Yiwen Yuan, Zecheng Zhang +6
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based dat…