Research on Tabular Deep Learning: Papers & Packages
A comprehensive toolkit and benchmark for tabular data learning, featuring 30+ deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
[ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"
Reactive datatables for large or small datasets
ML models + benchmark for tabular data classification and regression
Mambular is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.
Useful code for tabular modeling and automation.
R interface to fast.ai
TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning
Awesome Tabular Deep Learning for "Representation Learning for Tabular Data: A Comprehensive Survey"
Official PyTorch implementation of STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR 2023 Spotlight).
A framework for dynamically creating a tabular model based on an Excel template.
OLAP PivotTable Extensions is an Excel add-in which extends the functionality of PivotTables on Analysis Services cubes, the Excel Data Model (Power Pivot), and Power BI
This is a curated list of research on diffusion models for tabular data, and serves as the official repository for the survey paper "Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions"
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