Year: 2024
Type: preprint
Abstract: 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 data structure to handle complex tabular data, introducing a model abstraction to enable modular implementation of tabular models, and allowing external foundation models to be incorporated to handle complex columns (e.g., LLMs for text columns). We demonstrate the usefulness of PyTorch Frame by implementing diverse tabular models in a modular way, successfully applying these models to complex multi-modal tabular data, and integrating our framework with PyTorch Geometric, a PyTorch library for Graph Neural Networks (GNNs), to perform end-to-end learning over relational databases.
Source: arXiv (Cornell University)
Authors: Hu, Weihua, Yuan, Yiwen, Zhang, Zecheng, Nitta, Akihiro, Cao, Kaidi, Kocijan, Vid, Sunil, Jinu, Leskovec, Jure, Fey, Matthias
Institution: Stanford University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4393905663