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PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning

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arxiv 2404.00776 v2 pith:RL5CPIDY submitted 2024-03-31 cs.LG cs.DBstat.ML

Weihua Hu , Yiwen Yuan , Zecheng Zhang , Akihiro Nitta , Kaidi Cao , Vid Kocijan , Jinu Sunil , Jure Leskovec
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Matthias Fey
This is my paper
classification cs.LGcs.DBstat.ML
keywords tabularpytorchdataframelearningmodelscomplexframework
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Gradient-guided rewiring of foreign-key edges in relational databases degrades GNN predictions on regression tasks while preserving schema integrity constraints.

  2. Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases

    cs.DB 2026-02 conditional novelty 6.0 of 10

    A SQL-like domain-specific language that declares predictive tasks on relational databases and automatically generates leakage-free training labels, with batch and low-latency implementations.

  3. Large Language Models are Good Relational Learners

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Rel-LLM combines a GNN encoder with a frozen LLM via soft prompts and masked attribute pretraining, reporting improved average performance on RelBench relational database tasks.

  4. Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets

    cs.DB 2025-08 conditional novelty 4.0 of 10

    ReCoGNN automatically augments a base table with task-relevant features from related relational tables by splitting attributes into semantic sub-tables and propagating information through a weighted heterogeneous graph.

  5. PyG 2.0: Scalable Learning on Real World Graphs

    cs.LG 2025-07 conditional novelty 4.0 of 10

    PyG 2.0 is presented as a modular, scalable graph-learning framework with heterogeneous and temporal graph support, compilation-based speedups, and explainability.

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