mlsanityData to 7 Oct 2026

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

arXiv 2404.00776 ↗First posted 31 Mar 2024cs.LG cs.DB stat.MLcited by 32

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Abstract (arXiv)

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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 30 of its 37 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

30 of 37 references.

References · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  8. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  9. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  10. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  11. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  12. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
18 more
  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  2. 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
  3. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  4. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  5. 2020TabTransformer: Tabular Data Modeling Using Contextual Embeddingscited by 398
  6. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 358
  7. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 310
  8. 2019Neural Oblivious Decision Ensembles for Deep Learning on Tabular Datacited by 200
  9. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  10. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 185
  11. 2023XTab: Cross-table Pretraining for Tabular Transformerscited by 81
  12. 2023TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023cited by 73
  13. 2023Relational Deep Learning: Graph Representation Learning on Relational Databasescited by 55
  14. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 42
  15. 2023ExcelFormer: A neural network surpassing GBDTs on tabular datacited by 33
  16. 2021Benchmarking Multimodal AutoML for Tabular Data with Text Fieldscited by 24
  17. 2020DNF-Net: A Neural Architecture for Tabular Datacited by 17
  18. 2022Learning Backward Compatible Embeddingscited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 14; 2026: 11.

32 papers.

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  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  2. 2024RelBench: A Benchmark for Deep Learning on Relational Databasescited by 90
  3. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  4. 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 46
  5. 2025RelGNN: Composite Message Passing for Relational Deep Learningcited by 45
  6. 2025Griffin: Towards a Graph-Centric Relational Database Foundation Modelcited by 41
  7. 2025Relational Graph Transformercited by 30
  8. 2025Large Language Models are Good Relational Learnerscited by 22
  9. 2025Relational Deep Learning: Challenges, Foundations and Next-Generation Architecturescited by 22
  10. 2024ContextGNN: Beyond Two-Tower Recommendation Systemscited by 19
  11. 2026RelBench v2: A Large-Scale Benchmark and Repository for Relational Datacited by 17
  12. 2024AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Modelscited by 13
20 more
  1. 2026Relational In-Context Learning via Synthetic Pre-training with Structural Priorcited by 11
  2. 2024Transformers Meet Relational Databasescited by 9
  3. 2025RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databasescited by 8
  4. 2026Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databasescited by 8
  5. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 5
  6. 2025A Pre-training Framework for Relational Data with Information-theoretic Principlescited by 4
  7. 2025Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learningcited by 4
  8. 2026MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Imagecited by 2
  9. 2025RCCFormer: A Robust Crowd Counting Network Based on Transformercited by 1
  10. 2025Database Views as Explanations for Relational Deep Learningcited by 1
  11. 2025RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphscited by 1
  12. 2026Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databasescited by 1
  13. 2024Iterative Feature Exclusion Ranking for Deep Tabular Learningcited by 0
  14. 2025Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasetscited by 0
  15. 2026Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learningcited by 0
  16. 2026Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendationcited by 0
  17. 2026What Makes a Desired Graph for Relational Deep Learning?cited by 0
  18. 2026Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraintscited by 0
  19. 2026Incremental Evaluation and Training in Relational Deep Learningcited by 0
  20. 2026QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphscited by 0