PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912157305667584 |
|---|---|
| author | Hu, Weihua Yuan, Yiwen Zhang, Zecheng Nitta, Akihiro Cao, Kaidi Kocijan, Vid Sunil, Jinu Leskovec, Jure Fey, Matthias |
| author_facet | Hu, Weihua Yuan, Yiwen Zhang, Zecheng Nitta, Akihiro Cao, Kaidi Kocijan, Vid Sunil, Jinu Leskovec, Jure Fey, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning Hu, Weihua Yuan, Yiwen Zhang, Zecheng Nitta, Akihiro Cao, Kaidi Kocijan, Vid Sunil, Jinu Leskovec, Jure Fey, Matthias Machine Learning Databases 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. |
| title | PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2404.00776 |