4 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…
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…
ContextGNN: Beyond Two-Tower Recommendation Systems
Yiwen Yuan, Zecheng Zhang, Xinwei He +10
Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key l…